example of a literature review introduction

How to write a literature review introduction (+ examples)

The introduction to a literature review serves as your reader’s guide through your academic work and thought process. Explore the significance of literature review introductions in review papers, academic papers, essays, theses, and dissertations. We delve into the purpose and necessity of these introductions, explore the essential components of literature review introductions, and provide step-by-step guidance on how to craft your own, along with examples.

Why you need an introduction for a literature review

When you need an introduction for a literature review, what to include in a literature review introduction, examples of literature review introductions, steps to write your own literature review introduction.

A literature review is a comprehensive examination of the international academic literature concerning a particular topic. It involves summarizing published works, theories, and concepts while also highlighting gaps and offering critical reflections.

In academic writing , the introduction for a literature review is an indispensable component. Effective academic writing requires proper paragraph structuring to guide your reader through your argumentation. This includes providing an introduction to your literature review.

It is imperative to remember that you should never start sharing your findings abruptly. Even if there isn’t a dedicated introduction section .

Instead, you should always offer some form of introduction to orient the reader and clarify what they can expect.

There are three main scenarios in which you need an introduction for a literature review:

• Academic literature review papers: When your literature review constitutes the entirety of an academic review paper, a more substantial introduction is necessary. This introduction should resemble the standard introduction found in regular academic papers.
• Literature review section in an academic paper or essay: While this section tends to be brief, it’s important to precede the detailed literature review with a few introductory sentences. This helps orient the reader before delving into the literature itself.
• Literature review chapter or section in your thesis/dissertation: Every thesis and dissertation includes a literature review component, which also requires a concise introduction to set the stage for the subsequent review.

You may also like: How to write a fantastic thesis introduction (+15 examples)

It is crucial to customize the content and depth of your literature review introduction according to the specific format of your academic work.

In practical terms, this implies, for instance, that the introduction in an academic literature review paper, especially one derived from a systematic literature review , is quite comprehensive. Particularly compared to the rather brief one or two introductory sentences that are often found at the beginning of a literature review section in a standard academic paper. The introduction to the literature review chapter in a thesis or dissertation again adheres to different standards.

Here’s a structured breakdown based on length and the necessary information:

Academic literature review paper

The introduction of an academic literature review paper, which does not rely on empirical data, often necessitates a more extensive introduction than the brief literature review introductions typically found in empirical papers. It should encompass:

• The research problem: Clearly articulate the problem or question that your literature review aims to address.
• The research gap: Highlight the existing gaps, limitations, or unresolved aspects within the current body of literature related to the research problem.
• The research relevance: Explain why the chosen research problem and its subsequent investigation through a literature review are significant and relevant in your academic field.
• The literature review method: If applicable, describe the methodology employed in your literature review, especially if it is a systematic review or follows a specific research framework.
• The main findings or insights of the literature review: Summarize the key discoveries, insights, or trends that have emerged from your comprehensive review of the literature.
• The main argument of the literature review: Conclude the introduction by outlining the primary argument or statement that your literature review will substantiate, linking it to the research problem and relevance you’ve established.
• Preview of the literature review’s structure: Offer a glimpse into the organization of the literature review paper, acting as a guide for the reader. This overview outlines the subsequent sections of the paper and provides an understanding of what to anticipate.

By addressing these elements, your introduction will provide a clear and structured overview of what readers can expect in your literature review paper.

Regular literature review section in an academic article or essay

Most academic articles or essays incorporate regular literature review sections, often placed after the introduction. These sections serve to establish a scholarly basis for the research or discussion within the paper.

In a standard 8000-word journal article, the literature review section typically spans between 750 and 1250 words. The first few sentences or the first paragraph within this section often serve as an introduction. It should encompass:

• An introduction to the topic: When delving into the academic literature on a specific topic, it’s important to provide a smooth transition that aids the reader in comprehending why certain aspects will be discussed within your literature review.
• The core argument: While literature review sections primarily synthesize the work of other scholars, they should consistently connect to your central argument. This central argument serves as the crux of your message or the key takeaway you want your readers to retain. By positioning it at the outset of the literature review section and systematically substantiating it with evidence, you not only enhance reader comprehension but also elevate overall readability. This primary argument can typically be distilled into 1-2 succinct sentences.

In some cases, you might include:

• Methodology: Details about the methodology used, but only if your literature review employed a specialized method. If your approach involved a broader overview without a systematic methodology, you can omit this section, thereby conserving word count.

By addressing these elements, your introduction will effectively integrate your literature review into the broader context of your academic paper or essay. This will, in turn, assist your reader in seamlessly following your overarching line of argumentation.

Introduction to a literature review chapter in thesis or dissertation

The literature review typically constitutes a distinct chapter within a thesis or dissertation. Often, it is Chapter 2 of a thesis or dissertation.

Some students choose to incorporate a brief introductory section at the beginning of each chapter, including the literature review chapter. Alternatively, others opt to seamlessly integrate the introduction into the initial sentences of the literature review itself. Both approaches are acceptable, provided that you incorporate the following elements:

• Purpose of the literature review and its relevance to the thesis/dissertation research: Explain the broader objectives of the literature review within the context of your research and how it contributes to your thesis or dissertation. Essentially, you’re telling the reader why this literature review is important and how it fits into the larger scope of your academic work.
• Primary argument: Succinctly communicate what you aim to prove, explain, or explore through the review of existing literature. This statement helps guide the reader’s understanding of the review’s purpose and what to expect from it.
• Preview of the literature review’s content: Provide a brief overview of the topics or themes that your literature review will cover. It’s like a roadmap for the reader, outlining the main areas of focus within the review. This preview can help the reader anticipate the structure and organization of your literature review.
• Methodology: If your literature review involved a specific research method, such as a systematic review or meta-analysis, you should briefly describe that methodology. However, this is not always necessary, especially if your literature review is more of a narrative synthesis without a distinct research method.

By addressing these elements, your introduction will empower your literature review to play a pivotal role in your thesis or dissertation research. It will accomplish this by integrating your research into the broader academic literature and providing a solid theoretical foundation for your work.

Comprehending the art of crafting your own literature review introduction becomes significantly more accessible when you have concrete examples to examine. Here, you will find several examples that meet, or in most cases, adhere to the criteria described earlier.

Example 1: An effective introduction for an academic literature review paper

To begin, let’s delve into the introduction of an academic literature review paper. We will examine the paper “How does culture influence innovation? A systematic literature review”, which was published in 2018 in the journal Management Decision.

The entire introduction spans 611 words and is divided into five paragraphs. In this introduction, the authors accomplish the following:

• In the first paragraph, the authors introduce the broader topic of the literature review, which focuses on innovation and its significance in the context of economic competition. They underscore the importance of this topic, highlighting its relevance for both researchers and policymakers.
• In the second paragraph, the authors narrow down their focus to emphasize the specific role of culture in relation to innovation.
• In the third paragraph, the authors identify research gaps, noting that existing studies are often fragmented and disconnected. They then emphasize the value of conducting a systematic literature review to enhance our understanding of the topic.
• In the fourth paragraph, the authors introduce their specific objectives and explain how their insights can benefit other researchers and business practitioners.
• In the fifth and final paragraph, the authors provide an overview of the paper’s organization and structure.

In summary, this introduction stands as a solid example. While the authors deviate from previewing their key findings (which is a common practice at least in the social sciences), they do effectively cover all the other previously mentioned points.

Example 2: An effective introduction to a literature review section in an academic paper

The second example represents a typical academic paper, encompassing not only a literature review section but also empirical data, a case study, and other elements. We will closely examine the introduction to the literature review section in the paper “The environmentalism of the subalterns: a case study of environmental activism in Eastern Kurdistan/Rojhelat”, which was published in 2021 in the journal Local Environment.

The paper begins with a general introduction and then proceeds to the literature review, designated by the authors as their conceptual framework. Of particular interest is the first paragraph of this conceptual framework, comprising 142 words across five sentences:

“ A peripheral and marginalised nationality within a multinational though-Persian dominated Iranian society, the Kurdish people of Iranian Kurdistan (a region referred by the Kurds as Rojhelat/Eastern Kurdi-stan) have since the early twentieth century been subject to multifaceted and systematic discriminatory and exclusionary state policy in Iran. This condition has left a population of 12–15 million Kurds in Iran suffering from structural inequalities, disenfranchisement and deprivation. Mismanagement of Kurdistan’s natural resources and the degradation of its natural environmental are among examples of this disenfranchisement. As asserted by Julian Agyeman (2005), structural inequalities that sustain the domination of political and economic elites often simultaneously result in environmental degradation, injustice and discrimination against subaltern communities. This study argues that the environmental struggle in Eastern Kurdistan can be asserted as a (sub)element of the Kurdish liberation movement in Iran. Conceptually this research is inspired by and has been conducted through the lens of ‘subalternity’ ” ( Hassaniyan, 2021, p. 931 ).

In this first paragraph, the author is doing the following:

• The author contextualises the research
• The author links the research focus to the international literature on structural inequalities
• The author clearly presents the argument of the research
• The author clarifies how the research is inspired by and uses the concept of ‘subalternity’.

Thus, the author successfully introduces the literature review, from which point onward it dives into the main concept (‘subalternity’) of the research, and reviews the literature on socio-economic justice and environmental degradation.

While introductions to a literature review section aren’t always required to offer the same level of study context detail as demonstrated here, this introduction serves as a commendable model for orienting the reader within the literature review. It effectively underscores the literature review’s significance within the context of the study being conducted.

Examples 3-5: Effective introductions to literature review chapters

The introduction to a literature review chapter can vary in length, depending largely on the overall length of the literature review chapter itself. For example, a master’s thesis typically features a more concise literature review, thus necessitating a shorter introduction. In contrast, a Ph.D. thesis, with its more extensive literature review, often includes a more detailed introduction.

Numerous universities offer online repositories where you can access theses and dissertations from previous years, serving as valuable sources of reference. Many of these repositories, however, may require you to log in through your university account. Nevertheless, a few open-access repositories are accessible to anyone, such as the one by the University of Manchester . It’s important to note though that copyright restrictions apply to these resources, just as they would with published papers.

Master’s thesis literature review introduction

The first example is “Benchmarking Asymmetrical Heating Models of Spider Pulsar Companions” by P. Sun, a master’s thesis completed at the University of Manchester on January 9, 2024. The author, P. Sun, introduces the literature review chapter very briefly but effectively:

PhD thesis literature review chapter introduction

The second example is Deep Learning on Semi-Structured Data and its Applications to Video-Game AI, Woof, W. (Author). 31 Dec 2020, a PhD thesis completed at the University of Manchester . In Chapter 2, the author offers a comprehensive introduction to the topic in four paragraphs, with the final paragraph serving as an overview of the chapter’s structure:

PhD thesis literature review introduction

The last example is the doctoral thesis Metacognitive strategies and beliefs: Child correlates and early experiences Chan, K. Y. M. (Author). 31 Dec 2020 . The author clearly conducted a systematic literature review, commencing the review section with a discussion of the methodology and approach employed in locating and analyzing the selected records.

Having absorbed all of this information, let’s recap the essential steps and offer a succinct guide on how to proceed with creating your literature review introduction:

• Contextualize your review : Begin by clearly identifying the academic context in which your literature review resides and determining the necessary information to include.
• Outline your structure : Develop a structured outline for your literature review, highlighting the essential information you plan to incorporate in your introduction.
• Literature review process : Conduct a rigorous literature review, reviewing and analyzing relevant sources.
• Summarize and abstract : After completing the review, synthesize the findings and abstract key insights, trends, and knowledge gaps from the literature.
• Craft the introduction : Write your literature review introduction with meticulous attention to the seamless integration of your review into the larger context of your work. Ensure that your introduction effectively elucidates your rationale for the chosen review topics and the underlying reasons guiding your selection.

Master Academia

Get new content delivered directly to your inbox.

Subscribe and receive Master Academia's quarterly newsletter.

The best answers to "What are your plans for the future?"

10 tips for engaging your audience in academic writing, related articles.

10 key skills of successful master’s students

How to write effective cover letters for a paper submission

Dealing with failure as a PhD student

Reject decisions: Sample peer review comments and examples

Get science-backed answers as you write with Paperpal's Research feature

What is a Literature Review? How to Write It (with Examples)

A literature review is a critical analysis and synthesis of existing research on a particular topic. It provides an overview of the current state of knowledge, identifies gaps, and highlights key findings in the literature. 1 The purpose of a literature review is to situate your own research within the context of existing scholarship, demonstrating your understanding of the topic and showing how your work contributes to the ongoing conversation in the field. Learning how to write a literature review is a critical tool for successful research. Your ability to summarize and synthesize prior research pertaining to a certain topic demonstrates your grasp on the topic of study, and assists in the learning process. 

Table of Contents

• What is the purpose of literature review? 
• a. Habitat Loss and Species Extinction: 
• b. Range Shifts and Phenological Changes: 
• c. Ocean Acidification and Coral Reefs: 
• d. Adaptive Strategies and Conservation Efforts: 

How to write a good literature review 

• Choose a Topic and Define the Research Question: 
• Decide on the Scope of Your Review: 
• Select Databases for Searches: 
• Conduct Searches and Keep Track: 
• Review the Literature: 
• Organize and Write Your Literature Review: 
• How to write a literature review faster with Paperpal? 
• Frequently asked questions 

What is a literature review?

A well-conducted literature review demonstrates the researcher’s familiarity with the existing literature, establishes the context for their own research, and contributes to scholarly conversations on the topic. One of the purposes of a literature review is also to help researchers avoid duplicating previous work and ensure that their research is informed by and builds upon the existing body of knowledge.

What is the purpose of literature review?

A literature review serves several important purposes within academic and research contexts. Here are some key objectives and functions of a literature review: 2  

1. Contextualizing the Research Problem: The literature review provides a background and context for the research problem under investigation. It helps to situate the study within the existing body of knowledge. 

2. Identifying Gaps in Knowledge: By identifying gaps, contradictions, or areas requiring further research, the researcher can shape the research question and justify the significance of the study. This is crucial for ensuring that the new research contributes something novel to the field. 

Find academic papers related to your research topic faster. Try Research on Paperpal  

3. Understanding Theoretical and Conceptual Frameworks: Literature reviews help researchers gain an understanding of the theoretical and conceptual frameworks used in previous studies. This aids in the development of a theoretical framework for the current research. 

4. Providing Methodological Insights: Another purpose of literature reviews is that it allows researchers to learn about the methodologies employed in previous studies. This can help in choosing appropriate research methods for the current study and avoiding pitfalls that others may have encountered. 

5. Establishing Credibility: A well-conducted literature review demonstrates the researcher’s familiarity with existing scholarship, establishing their credibility and expertise in the field. It also helps in building a solid foundation for the new research. 

6. Informing Hypotheses or Research Questions: The literature review guides the formulation of hypotheses or research questions by highlighting relevant findings and areas of uncertainty in existing literature. 

Literature review example

Let’s delve deeper with a literature review example: Let’s say your literature review is about the impact of climate change on biodiversity. You might format your literature review into sections such as the effects of climate change on habitat loss and species extinction, phenological changes, and marine biodiversity. Each section would then summarize and analyze relevant studies in those areas, highlighting key findings and identifying gaps in the research. The review would conclude by emphasizing the need for further research on specific aspects of the relationship between climate change and biodiversity. The following literature review template provides a glimpse into the recommended literature review structure and content, demonstrating how research findings are organized around specific themes within a broader topic. 

Literature Review on Climate Change Impacts on Biodiversity:

Climate change is a global phenomenon with far-reaching consequences, including significant impacts on biodiversity. This literature review synthesizes key findings from various studies: 

a. Habitat Loss and Species Extinction:

Climate change-induced alterations in temperature and precipitation patterns contribute to habitat loss, affecting numerous species (Thomas et al., 2004). The review discusses how these changes increase the risk of extinction, particularly for species with specific habitat requirements. 

b. Range Shifts and Phenological Changes:

Observations of range shifts and changes in the timing of biological events (phenology) are documented in response to changing climatic conditions (Parmesan & Yohe, 2003). These shifts affect ecosystems and may lead to mismatches between species and their resources. 

c. Ocean Acidification and Coral Reefs:

The review explores the impact of climate change on marine biodiversity, emphasizing ocean acidification’s threat to coral reefs (Hoegh-Guldberg et al., 2007). Changes in pH levels negatively affect coral calcification, disrupting the delicate balance of marine ecosystems. 

d. Adaptive Strategies and Conservation Efforts:

Recognizing the urgency of the situation, the literature review discusses various adaptive strategies adopted by species and conservation efforts aimed at mitigating the impacts of climate change on biodiversity (Hannah et al., 2007). It emphasizes the importance of interdisciplinary approaches for effective conservation planning. 

Strengthen your literature review with factual insights. Try Research on Paperpal for free!    

Writing a literature review involves summarizing and synthesizing existing research on a particular topic. A good literature review format should include the following elements. 

Introduction: The introduction sets the stage for your literature review, providing context and introducing the main focus of your review. 

• Opening Statement: Begin with a general statement about the broader topic and its significance in the field. 
• Scope and Purpose: Clearly define the scope of your literature review. Explain the specific research question or objective you aim to address. 
• Organizational Framework: Briefly outline the structure of your literature review, indicating how you will categorize and discuss the existing research. 
• Significance of the Study: Highlight why your literature review is important and how it contributes to the understanding of the chosen topic. 
• Thesis Statement: Conclude the introduction with a concise thesis statement that outlines the main argument or perspective you will develop in the body of the literature review. 

Body: The body of the literature review is where you provide a comprehensive analysis of existing literature, grouping studies based on themes, methodologies, or other relevant criteria. 

• Organize by Theme or Concept: Group studies that share common themes, concepts, or methodologies. Discuss each theme or concept in detail, summarizing key findings and identifying gaps or areas of disagreement. 
• Critical Analysis: Evaluate the strengths and weaknesses of each study. Discuss the methodologies used, the quality of evidence, and the overall contribution of each work to the understanding of the topic. 
• Synthesis of Findings: Synthesize the information from different studies to highlight trends, patterns, or areas of consensus in the literature. 
• Identification of Gaps: Discuss any gaps or limitations in the existing research and explain how your review contributes to filling these gaps. 
• Transition between Sections: Provide smooth transitions between different themes or concepts to maintain the flow of your literature review. 

Write and Cite as you go with Paperpal Research. Start now for free.   

Conclusion: The conclusion of your literature review should summarize the main findings, highlight the contributions of the review, and suggest avenues for future research. 

• Summary of Key Findings: Recap the main findings from the literature and restate how they contribute to your research question or objective. 
• Contributions to the Field: Discuss the overall contribution of your literature review to the existing knowledge in the field. 
• Implications and Applications: Explore the practical implications of the findings and suggest how they might impact future research or practice. 
• Recommendations for Future Research: Identify areas that require further investigation and propose potential directions for future research in the field. 
• Final Thoughts: Conclude with a final reflection on the importance of your literature review and its relevance to the broader academic community. 

Conducting a literature review

Conducting a literature review is an essential step in research that involves reviewing and analyzing existing literature on a specific topic. It’s important to know how to do a literature review effectively, so here are the steps to follow: 1  

Choose a Topic and Define the Research Question:

• Select a topic that is relevant to your field of study. 
• Clearly define your research question or objective. Determine what specific aspect of the topic do you want to explore? 

Decide on the Scope of Your Review:

• Determine the timeframe for your literature review. Are you focusing on recent developments, or do you want a historical overview? 
• Consider the geographical scope. Is your review global, or are you focusing on a specific region? 
• Define the inclusion and exclusion criteria. What types of sources will you include? Are there specific types of studies or publications you will exclude? 

Select Databases for Searches:

• Identify relevant databases for your field. Examples include PubMed, IEEE Xplore, Scopus, Web of Science, and Google Scholar. 
• Consider searching in library catalogs, institutional repositories, and specialized databases related to your topic. 

Conduct Searches and Keep Track:

• Develop a systematic search strategy using keywords, Boolean operators (AND, OR, NOT), and other search techniques. 
• Record and document your search strategy for transparency and replicability. 
• Keep track of the articles, including publication details, abstracts, and links. Use citation management tools like EndNote, Zotero, or Mendeley to organize your references. 

Review the Literature:

• Evaluate the relevance and quality of each source. Consider the methodology, sample size, and results of studies. 
• Organize the literature by themes or key concepts. Identify patterns, trends, and gaps in the existing research. 
• Summarize key findings and arguments from each source. Compare and contrast different perspectives. 
• Identify areas where there is a consensus in the literature and where there are conflicting opinions. 
• Provide critical analysis and synthesis of the literature. What are the strengths and weaknesses of existing research? 

Organize and Write Your Literature Review:

• Literature review outline should be based on themes, chronological order, or methodological approaches. 
• Write a clear and coherent narrative that synthesizes the information gathered. 
• Use proper citations for each source and ensure consistency in your citation style (APA, MLA, Chicago, etc.). 
• Conclude your literature review by summarizing key findings, identifying gaps, and suggesting areas for future research. 

Whether you’re exploring a new research field or finding new angles to develop an existing topic, sifting through hundreds of papers can take more time than you have to spare. But what if you could find science-backed insights with verified citations in seconds? That’s the power of Paperpal’s new Research feature!  

How to write a literature review faster with Paperpal?

Paperpal, an AI writing assistant, integrates powerful academic search capabilities within its writing platform. With the Research feature, you get 100% factual insights, with citations backed by 250M+ verified research articles, directly within your writing interface with the option to save relevant references in your Citation Library. By eliminating the need to switch tabs to find answers to all your research questions, Paperpal saves time and helps you stay focused on your writing.   

Here’s how to use the Research feature:  

• Ask a question: Get started with a new document on paperpal.com. Click on the “Research” feature and type your question in plain English. Paperpal will scour over 250 million research articles, including conference papers and preprints, to provide you with accurate insights and citations. 
• Review and Save: Paperpal summarizes the information, while citing sources and listing relevant reads. You can quickly scan the results to identify relevant references and save these directly to your built-in citations library for later access. 
• Cite with Confidence: Paperpal makes it easy to incorporate relevant citations and references into your writing, ensuring your arguments are well-supported by credible sources. This translates to a polished, well-researched literature review. 

The literature review sample and detailed advice on writing and conducting a review will help you produce a well-structured report. But remember that a good literature review is an ongoing process, and it may be necessary to revisit and update it as your research progresses. By combining effortless research with an easy citation process, Paperpal Research streamlines the literature review process and empowers you to write faster and with more confidence. Try Paperpal Research now and see for yourself.  

Frequently asked questions

A literature review is a critical and comprehensive analysis of existing literature (published and unpublished works) on a specific topic or research question and provides a synthesis of the current state of knowledge in a particular field. A well-conducted literature review is crucial for researchers to build upon existing knowledge, avoid duplication of efforts, and contribute to the advancement of their field. It also helps researchers situate their work within a broader context and facilitates the development of a sound theoretical and conceptual framework for their studies.

Literature review is a crucial component of research writing, providing a solid background for a research paper’s investigation. The aim is to keep professionals up to date by providing an understanding of ongoing developments within a specific field, including research methods, and experimental techniques used in that field, and present that knowledge in the form of a written report. Also, the depth and breadth of the literature review emphasizes the credibility of the scholar in his or her field.  

Before writing a literature review, it’s essential to undertake several preparatory steps to ensure that your review is well-researched, organized, and focused. This includes choosing a topic of general interest to you and doing exploratory research on that topic, writing an annotated bibliography, and noting major points, especially those that relate to the position you have taken on the topic. 

Literature reviews and academic research papers are essential components of scholarly work but serve different purposes within the academic realm. 3 A literature review aims to provide a foundation for understanding the current state of research on a particular topic, identify gaps or controversies, and lay the groundwork for future research. Therefore, it draws heavily from existing academic sources, including books, journal articles, and other scholarly publications. In contrast, an academic research paper aims to present new knowledge, contribute to the academic discourse, and advance the understanding of a specific research question. Therefore, it involves a mix of existing literature (in the introduction and literature review sections) and original data or findings obtained through research methods. 

Literature reviews are essential components of academic and research papers, and various strategies can be employed to conduct them effectively. If you want to know how to write a literature review for a research paper, here are four common approaches that are often used by researchers.  Chronological Review: This strategy involves organizing the literature based on the chronological order of publication. It helps to trace the development of a topic over time, showing how ideas, theories, and research have evolved.  Thematic Review: Thematic reviews focus on identifying and analyzing themes or topics that cut across different studies. Instead of organizing the literature chronologically, it is grouped by key themes or concepts, allowing for a comprehensive exploration of various aspects of the topic.  Methodological Review: This strategy involves organizing the literature based on the research methods employed in different studies. It helps to highlight the strengths and weaknesses of various methodologies and allows the reader to evaluate the reliability and validity of the research findings.  Theoretical Review: A theoretical review examines the literature based on the theoretical frameworks used in different studies. This approach helps to identify the key theories that have been applied to the topic and assess their contributions to the understanding of the subject.  It’s important to note that these strategies are not mutually exclusive, and a literature review may combine elements of more than one approach. The choice of strategy depends on the research question, the nature of the literature available, and the goals of the review. Additionally, other strategies, such as integrative reviews or systematic reviews, may be employed depending on the specific requirements of the research.

The literature review format can vary depending on the specific publication guidelines. However, there are some common elements and structures that are often followed. Here is a general guideline for the format of a literature review:  Introduction:   Provide an overview of the topic.  Define the scope and purpose of the literature review.  State the research question or objective.  Body:   Organize the literature by themes, concepts, or chronology.  Critically analyze and evaluate each source.  Discuss the strengths and weaknesses of the studies.  Highlight any methodological limitations or biases.  Identify patterns, connections, or contradictions in the existing research.  Conclusion:   Summarize the key points discussed in the literature review.  Highlight the research gap.  Address the research question or objective stated in the introduction.  Highlight the contributions of the review and suggest directions for future research.

Both annotated bibliographies and literature reviews involve the examination of scholarly sources. While annotated bibliographies focus on individual sources with brief annotations, literature reviews provide a more in-depth, integrated, and comprehensive analysis of existing literature on a specific topic. The key differences are as follows: 

References 

• Denney, A. S., & Tewksbury, R. (2013). How to write a literature review.  Journal of criminal justice education ,  24 (2), 218-234. 
• Pan, M. L. (2016).  Preparing literature reviews: Qualitative and quantitative approaches . Taylor & Francis. 
• Cantero, C. (2019). How to write a literature review.  San José State University Writing Center . 

Paperpal is an AI writing assistant that help academics write better, faster with real-time suggestions for in-depth language and grammar correction. Trained on millions of research manuscripts enhanced by professional academic editors, Paperpal delivers human precision at machine speed.  

Try it for free or upgrade to  Paperpal Prime , which unlocks unlimited access to premium features like academic translation, paraphrasing, contextual synonyms, consistency checks and more. It’s like always having a professional academic editor by your side! Go beyond limitations and experience the future of academic writing.  Get Paperpal Prime now at just US$19 a month!

Related Reads:

• Empirical Research: A Comprehensive Guide for Academics 
• How to Write a Scientific Paper in 10 Steps 
• How Long Should a Chapter Be?
• How to Use Paperpal to Generate Emails & Cover Letters?

6 Tips for Post-Doc Researchers to Take Their Career to the Next Level

Self-plagiarism in research: what it is and how to avoid it, you may also like, how to write a high-quality conference paper, how paperpal’s research feature helps you develop and..., how paperpal is enhancing academic productivity and accelerating..., how to write a successful book chapter for..., academic editing: how to self-edit academic text with..., 4 ways paperpal encourages responsible writing with ai, what are scholarly sources and where can you..., how to write a hypothesis types and examples , measuring academic success: definition & strategies for excellence, what is academic writing: tips for students.

• UConn Library
• Literature Review: The What, Why and How-to Guide
• Introduction

Literature Review: The What, Why and How-to Guide — Introduction

• Getting Started
• How to Pick a Topic
• Strategies to Find Sources
• Evaluating Sources & Lit. Reviews
• Tips for Writing Literature Reviews
• Writing Literature Review: Useful Sites
• Citation Resources
• Other Academic Writings

What are Literature Reviews?

So, what is a literature review? "A literature review is an account of what has been published on a topic by accredited scholars and researchers. In writing the literature review, your purpose is to convey to your reader what knowledge and ideas have been established on a topic, and what their strengths and weaknesses are. As a piece of writing, the literature review must be defined by a guiding concept (e.g., your research objective, the problem or issue you are discussing, or your argumentative thesis). It is not just a descriptive list of the material available, or a set of summaries." Taylor, D.  The literature review: A few tips on conducting it . University of Toronto Health Sciences Writing Centre.

Goals of Literature Reviews

What are the goals of creating a Literature Review?  A literature could be written to accomplish different aims:

• To develop a theory or evaluate an existing theory
• To summarize the historical or existing state of a research topic
• Identify a problem in a field of research 

Baumeister, R. F., & Leary, M. R. (1997). Writing narrative literature reviews .  Review of General Psychology , 1 (3), 311-320.

What kinds of sources require a Literature Review?

• A research paper assigned in a course
• A thesis or dissertation
• A grant proposal
• An article intended for publication in a journal

All these instances require you to collect what has been written about your research topic so that you can demonstrate how your own research sheds new light on the topic.

Types of Literature Reviews

What kinds of literature reviews are written?

Narrative review: The purpose of this type of review is to describe the current state of the research on a specific topic/research and to offer a critical analysis of the literature reviewed. Studies are grouped by research/theoretical categories, and themes and trends, strengths and weakness, and gaps are identified. The review ends with a conclusion section which summarizes the findings regarding the state of the research of the specific study, the gaps identify and if applicable, explains how the author's research will address gaps identify in the review and expand the knowledge on the topic reviewed.

• Example : Predictors and Outcomes of U.S. Quality Maternity Leave: A Review and Conceptual Framework:  10.1177/08948453211037398  

Systematic review : "The authors of a systematic review use a specific procedure to search the research literature, select the studies to include in their review, and critically evaluate the studies they find." (p. 139). Nelson, L. K. (2013). Research in Communication Sciences and Disorders . Plural Publishing.

• Example : The effect of leave policies on increasing fertility: a systematic review:  10.1057/s41599-022-01270-w

Meta-analysis : "Meta-analysis is a method of reviewing research findings in a quantitative fashion by transforming the data from individual studies into what is called an effect size and then pooling and analyzing this information. The basic goal in meta-analysis is to explain why different outcomes have occurred in different studies." (p. 197). Roberts, M. C., & Ilardi, S. S. (2003). Handbook of Research Methods in Clinical Psychology . Blackwell Publishing.

• Example : Employment Instability and Fertility in Europe: A Meta-Analysis:  10.1215/00703370-9164737

Meta-synthesis : "Qualitative meta-synthesis is a type of qualitative study that uses as data the findings from other qualitative studies linked by the same or related topic." (p.312). Zimmer, L. (2006). Qualitative meta-synthesis: A question of dialoguing with texts .  Journal of Advanced Nursing , 53 (3), 311-318.

• Example : Women’s perspectives on career successes and barriers: A qualitative meta-synthesis:  10.1177/05390184221113735

Literature Reviews in the Health Sciences

• UConn Health subject guide on systematic reviews Explanation of the different review types used in health sciences literature as well as tools to help you find the right review type
• << Previous: Getting Started
• Next: How to Pick a Topic >>
• Last Updated: Sep 21, 2022 2:16 PM
• URL: https://guides.lib.uconn.edu/literaturereview

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Dissertation
• What is a Literature Review? | Guide, Template, & Examples

What is a Literature Review? | Guide, Template, & Examples

Published on 22 February 2022 by Shona McCombes . Revised on 7 June 2022.

What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research.

There are five key steps to writing a literature review:

• Search for relevant literature
• Evaluate sources
• Identify themes, debates and gaps
• Outline the structure
• Write your literature review

A good literature review doesn’t just summarise sources – it analyses, synthesises, and critically evaluates to give a clear picture of the state of knowledge on the subject.

Instantly correct all language mistakes in your text

Be assured that you'll submit flawless writing. Upload your document to correct all your mistakes.

Table of contents

Why write a literature review, examples of literature reviews, step 1: search for relevant literature, step 2: evaluate and select sources, step 3: identify themes, debates and gaps, step 4: outline your literature review’s structure, step 5: write your literature review, frequently asked questions about literature reviews, introduction.

• Quick Run-through
• Step 1 & 2

When you write a dissertation or thesis, you will have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:

• Demonstrate your familiarity with the topic and scholarly context
• Develop a theoretical framework and methodology for your research
• Position yourself in relation to other researchers and theorists
• Show how your dissertation addresses a gap or contributes to a debate

You might also have to write a literature review as a stand-alone assignment. In this case, the purpose is to evaluate the current state of research and demonstrate your knowledge of scholarly debates around a topic.

The content will look slightly different in each case, but the process of conducting a literature review follows the same steps. We’ve written a step-by-step guide that you can follow below.

The only proofreading tool specialized in correcting academic writing

The academic proofreading tool has been trained on 1000s of academic texts and by native English editors. Making it the most accurate and reliable proofreading tool for students.

Correct my document today

Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.

• Example literature review #1: “Why Do People Migrate? A Review of the Theoretical Literature” ( Theoretical literature review about the development of economic migration theory from the 1950s to today.)
• Example literature review #2: “Literature review as a research methodology: An overview and guidelines” ( Methodological literature review about interdisciplinary knowledge acquisition and production.)
• Example literature review #3: “The Use of Technology in English Language Learning: A Literature Review” ( Thematic literature review about the effects of technology on language acquisition.)
• Example literature review #4: “Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review” ( Chronological literature review about how the concept of listening skills has changed over time.)

You can also check out our templates with literature review examples and sample outlines at the links below.

Download Word doc Download Google doc

Before you begin searching for literature, you need a clearly defined topic .

If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research objectives and questions .

If you are writing a literature review as a stand-alone assignment, you will have to choose a focus and develop a central question to direct your search. Unlike a dissertation research question, this question has to be answerable without collecting original data. You should be able to answer it based only on a review of existing publications.

Make a list of keywords

Start by creating a list of keywords related to your research topic. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list if you discover new keywords in the process of your literature search.

• Social media, Facebook, Instagram, Twitter, Snapchat, TikTok
• Body image, self-perception, self-esteem, mental health
• Generation Z, teenagers, adolescents, youth

Search for relevant sources

Use your keywords to begin searching for sources. Some databases to search for journals and articles include:

• Your university’s library catalogue
• Google Scholar
• Project Muse (humanities and social sciences)
• Medline (life sciences and biomedicine)
• EconLit (economics)
• Inspec (physics, engineering and computer science)

You can use boolean operators to help narrow down your search:

Read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.

To identify the most important publications on your topic, take note of recurring citations. If the same authors, books or articles keep appearing in your reading, make sure to seek them out.

You probably won’t be able to read absolutely everything that has been written on the topic – you’ll have to evaluate which sources are most relevant to your questions.

For each publication, ask yourself:

• What question or problem is the author addressing?
• What are the key concepts and how are they defined?
• What are the key theories, models and methods? Does the research use established frameworks or take an innovative approach?
• What are the results and conclusions of the study?
• How does the publication relate to other literature in the field? Does it confirm, add to, or challenge established knowledge?
• How does the publication contribute to your understanding of the topic? What are its key insights and arguments?
• What are the strengths and weaknesses of the research?

Make sure the sources you use are credible, and make sure you read any landmark studies and major theories in your field of research.

You can find out how many times an article has been cited on Google Scholar – a high citation count means the article has been influential in the field, and should certainly be included in your literature review.

The scope of your review will depend on your topic and discipline: in the sciences you usually only review recent literature, but in the humanities you might take a long historical perspective (for example, to trace how a concept has changed in meaning over time).

Remember that you can use our template to summarise and evaluate sources you’re thinking about using!

Take notes and cite your sources

As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.

It’s important to keep track of your sources with references to avoid plagiarism . It can be helpful to make an annotated bibliography, where you compile full reference information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.

You can use our free APA Reference Generator for quick, correct, consistent citations.

To begin organising your literature review’s argument and structure, you need to understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:

• Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
• Themes: what questions or concepts recur across the literature?
• Debates, conflicts and contradictions: where do sources disagree?
• Pivotal publications: are there any influential theories or studies that changed the direction of the field?
• Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?

This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.

• Most research has focused on young women.
• There is an increasing interest in the visual aspects of social media.
• But there is still a lack of robust research on highly-visual platforms like Instagram and Snapchat – this is a gap that you could address in your own research.

There are various approaches to organising the body of a literature review. You should have a rough idea of your strategy before you start writing.

Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).

Chronological

The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarising sources in order.

Try to analyse patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.

If you have found some recurring central themes, you can organise your literature review into subsections that address different aspects of the topic.

For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.

Methodological

If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:

• Look at what results have emerged in qualitative versus quantitative research
• Discuss how the topic has been approached by empirical versus theoretical scholarship
• Divide the literature into sociological, historical, and cultural sources

Theoretical

A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.

You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.

Like any other academic text, your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.

The introduction should clearly establish the focus and purpose of the literature review.

If you are writing the literature review as part of your dissertation or thesis, reiterate your central problem or research question and give a brief summary of the scholarly context. You can emphasise the timeliness of the topic (“many recent studies have focused on the problem of x”) or highlight a gap in the literature (“while there has been much research on x, few researchers have taken y into consideration”).

Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.

As you write, make sure to follow these tips:

• Summarise and synthesise: give an overview of the main points of each source and combine them into a coherent whole.
• Analyse and interpret: don’t just paraphrase other researchers – add your own interpretations, discussing the significance of findings in relation to the literature as a whole.
• Critically evaluate: mention the strengths and weaknesses of your sources.
• Write in well-structured paragraphs: use transitions and topic sentences to draw connections, comparisons and contrasts.

In the conclusion, you should summarise the key findings you have taken from the literature and emphasise their significance.

If the literature review is part of your dissertation or thesis, reiterate how your research addresses gaps and contributes new knowledge, or discuss how you have drawn on existing theories and methods to build a framework for your research. This can lead directly into your methodology section.

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a dissertation , thesis, research paper , or proposal .

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarise yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

The literature review usually comes near the beginning of your  dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2022, June 07). What is a Literature Review? | Guide, Template, & Examples. Scribbr. Retrieved 14 May 2024, from https://www.scribbr.co.uk/thesis-dissertation/literature-review/

Is this article helpful?

Shona McCombes

Other students also liked, how to write a dissertation proposal | a step-by-step guide, what is a theoretical framework | a step-by-step guide, what is a research methodology | steps & tips.

Literature Review Guide: Examples of Literature Reviews

• What is a Literature Review?
• How to start?
• Search strategies and Databases
• Examples of Literature Reviews
• How to organise the review
• Library summary
• Emerald Infographic

All good quality journal articles will include a small Literature Review after the Introduction paragraph.  It may not be called a Literature Review but gives you an idea of how one is created in miniature.

Sample Literature Reviews as part of a articles or Theses

• Sample Literature Review on Critical Thinking (Gwendolyn Reece, American University Library)
• Hackett, G and Melia, D . The hotel as the holiday/stay destination:trends and innovations. Presented at TRIC Conference, Belfast, Ireland- June 2012 and EuroCHRIE Conference

Links to sample Literature Reviews from other libraries

• Sample literature reviews from University of West Florida

Standalone Literature Reviews

• Attitudes towards the Disability in Ireland
• Martin, A., O'Connor-Fenelon, M. and Lyons, R. (2010). Non-verbal communication between nurses and people with an intellectual disability: A review of the literature. Journal of Intellectual Diabilities, 14(4), 303-314.

Irish Theses

• Phillips, Martin (2015) European airline performance: a data envelopment analysis with extrapolations based on model outputs. Master of Business Studies thesis, Dublin City University.
• The customers’ perception of servicescape’s influence on their behaviours, in the food retail industry : Dublin Business School 2015
• Coughlan, Ray (2015) What was the role of leadership in the transformation of a failing Irish Insurance business. Masters thesis, Dublin, National College of Ireland.
• << Previous: Search strategies and Databases
• Next: Tutorials >>
• Last Updated: Feb 27, 2024 4:07 PM
• URL: https://ait.libguides.com/literaturereview

The Sheridan Libraries

• Write a Literature Review
• Sheridan Libraries
• Find This link opens in a new window
• Evaluate This link opens in a new window

What Will You Do Differently?

Please help your librarians by filling out this two-minute survey of today's class session..

Professor, this one's for you .

Introduction

Literature reviews take time. here is some general information to know before you start.  .

•  VIDEO -- This video is a great overview of the entire process.  (2020; North Carolina State University Libraries) --The transcript is included --This is for everyone; ignore the mention of "graduate students" --9.5 minutes, and every second is important  
• OVERVIEW -- Read this page from Purdue's OWL. It's not long, and gives some tips to fill in what you just learned from the video.  
• NOT A RESEARCH ARTICLE -- A literature review follows a different style, format, and structure from a research article.  

Steps to Completing a Literature Review

• Next: Find >>
• Last Updated: Sep 26, 2023 10:25 AM
• URL: https://guides.library.jhu.edu/lit-review

Purdue Online Writing Lab Purdue OWL® College of Liberal Arts

Writing a Literature Review

Welcome to the Purdue OWL

This page is brought to you by the OWL at Purdue University. When printing this page, you must include the entire legal notice.

Copyright ©1995-2018 by The Writing Lab & The OWL at Purdue and Purdue University. All rights reserved. This material may not be published, reproduced, broadcast, rewritten, or redistributed without permission. Use of this site constitutes acceptance of our terms and conditions of fair use.

A literature review is a document or section of a document that collects key sources on a topic and discusses those sources in conversation with each other (also called synthesis ). The lit review is an important genre in many disciplines, not just literature (i.e., the study of works of literature such as novels and plays). When we say “literature review” or refer to “the literature,” we are talking about the research ( scholarship ) in a given field. You will often see the terms “the research,” “the scholarship,” and “the literature” used mostly interchangeably.

Where, when, and why would I write a lit review?

There are a number of different situations where you might write a literature review, each with slightly different expectations; different disciplines, too, have field-specific expectations for what a literature review is and does. For instance, in the humanities, authors might include more overt argumentation and interpretation of source material in their literature reviews, whereas in the sciences, authors are more likely to report study designs and results in their literature reviews; these differences reflect these disciplines’ purposes and conventions in scholarship. You should always look at examples from your own discipline and talk to professors or mentors in your field to be sure you understand your discipline’s conventions, for literature reviews as well as for any other genre.

A literature review can be a part of a research paper or scholarly article, usually falling after the introduction and before the research methods sections. In these cases, the lit review just needs to cover scholarship that is important to the issue you are writing about; sometimes it will also cover key sources that informed your research methodology.

Lit reviews can also be standalone pieces, either as assignments in a class or as publications. In a class, a lit review may be assigned to help students familiarize themselves with a topic and with scholarship in their field, get an idea of the other researchers working on the topic they’re interested in, find gaps in existing research in order to propose new projects, and/or develop a theoretical framework and methodology for later research. As a publication, a lit review usually is meant to help make other scholars’ lives easier by collecting and summarizing, synthesizing, and analyzing existing research on a topic. This can be especially helpful for students or scholars getting into a new research area, or for directing an entire community of scholars toward questions that have not yet been answered.

What are the parts of a lit review?

Most lit reviews use a basic introduction-body-conclusion structure; if your lit review is part of a larger paper, the introduction and conclusion pieces may be just a few sentences while you focus most of your attention on the body. If your lit review is a standalone piece, the introduction and conclusion take up more space and give you a place to discuss your goals, research methods, and conclusions separately from where you discuss the literature itself.

Introduction:

• An introductory paragraph that explains what your working topic and thesis is
• A forecast of key topics or texts that will appear in the review
• Potentially, a description of how you found sources and how you analyzed them for inclusion and discussion in the review (more often found in published, standalone literature reviews than in lit review sections in an article or research paper)
• Summarize and synthesize: Give an overview of the main points of each source and combine them into a coherent whole
• Analyze and interpret: Don’t just paraphrase other researchers – add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
• Critically Evaluate: Mention the strengths and weaknesses of your sources
• Write in well-structured paragraphs: Use transition words and topic sentence to draw connections, comparisons, and contrasts.

Conclusion:

• Summarize the key findings you have taken from the literature and emphasize their significance
• Connect it back to your primary research question

How should I organize my lit review?

Lit reviews can take many different organizational patterns depending on what you are trying to accomplish with the review. Here are some examples:

• Chronological : The simplest approach is to trace the development of the topic over time, which helps familiarize the audience with the topic (for instance if you are introducing something that is not commonly known in your field). If you choose this strategy, be careful to avoid simply listing and summarizing sources in order. Try to analyze the patterns, turning points, and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred (as mentioned previously, this may not be appropriate in your discipline — check with a teacher or mentor if you’re unsure).
• Thematic : If you have found some recurring central themes that you will continue working with throughout your piece, you can organize your literature review into subsections that address different aspects of the topic. For example, if you are reviewing literature about women and religion, key themes can include the role of women in churches and the religious attitude towards women.
• Qualitative versus quantitative research
• Empirical versus theoretical scholarship
• Divide the research by sociological, historical, or cultural sources
• Theoretical : In many humanities articles, the literature review is the foundation for the theoretical framework. You can use it to discuss various theories, models, and definitions of key concepts. You can argue for the relevance of a specific theoretical approach or combine various theorical concepts to create a framework for your research.

What are some strategies or tips I can use while writing my lit review?

Any lit review is only as good as the research it discusses; make sure your sources are well-chosen and your research is thorough. Don’t be afraid to do more research if you discover a new thread as you’re writing. More info on the research process is available in our "Conducting Research" resources .

As you’re doing your research, create an annotated bibliography ( see our page on the this type of document ). Much of the information used in an annotated bibliography can be used also in a literature review, so you’ll be not only partially drafting your lit review as you research, but also developing your sense of the larger conversation going on among scholars, professionals, and any other stakeholders in your topic.

Usually you will need to synthesize research rather than just summarizing it. This means drawing connections between sources to create a picture of the scholarly conversation on a topic over time. Many student writers struggle to synthesize because they feel they don’t have anything to add to the scholars they are citing; here are some strategies to help you:

• It often helps to remember that the point of these kinds of syntheses is to show your readers how you understand your research, to help them read the rest of your paper.
• Writing teachers often say synthesis is like hosting a dinner party: imagine all your sources are together in a room, discussing your topic. What are they saying to each other?
• Look at the in-text citations in each paragraph. Are you citing just one source for each paragraph? This usually indicates summary only. When you have multiple sources cited in a paragraph, you are more likely to be synthesizing them (not always, but often
• Read more about synthesis here.

The most interesting literature reviews are often written as arguments (again, as mentioned at the beginning of the page, this is discipline-specific and doesn’t work for all situations). Often, the literature review is where you can establish your research as filling a particular gap or as relevant in a particular way. You have some chance to do this in your introduction in an article, but the literature review section gives a more extended opportunity to establish the conversation in the way you would like your readers to see it. You can choose the intellectual lineage you would like to be part of and whose definitions matter most to your thinking (mostly humanities-specific, but this goes for sciences as well). In addressing these points, you argue for your place in the conversation, which tends to make the lit review more compelling than a simple reporting of other sources.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• CAREER FEATURE
• 04 December 2020
• Correction 09 December 2020

How to write a superb literature review

Andy Tay is a freelance writer based in Singapore.

You can also search for this author in PubMed   Google Scholar

Literature reviews are important resources for scientists. They provide historical context for a field while offering opinions on its future trajectory. Creating them can provide inspiration for one’s own research, as well as some practice in writing. But few scientists are trained in how to write a review — or in what constitutes an excellent one. Even picking the appropriate software to use can be an involved decision (see ‘Tools and techniques’). So Nature asked editors and working scientists with well-cited reviews for their tips.

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 51 print issues and online access

185,98 € per year

only 3,65 € per issue

Rent or buy this article

Prices vary by article type

Prices may be subject to local taxes which are calculated during checkout

doi: https://doi.org/10.1038/d41586-020-03422-x

Interviews have been edited for length and clarity.

Updates & Corrections

Correction 09 December 2020 : An earlier version of the tables in this article included some incorrect details about the programs Zotero, Endnote and Manubot. These have now been corrected.

Hsing, I.-M., Xu, Y. & Zhao, W. Electroanalysis 19 , 755–768 (2007).

Article   Google Scholar  

Ledesma, H. A. et al. Nature Nanotechnol. 14 , 645–657 (2019).

Article   PubMed   Google Scholar  

Brahlek, M., Koirala, N., Bansal, N. & Oh, S. Solid State Commun. 215–216 , 54–62 (2015).

Choi, Y. & Lee, S. Y. Nature Rev. Chem . https://doi.org/10.1038/s41570-020-00221-w (2020).

Download references

Related Articles

• Research management

I’m worried I’ve been contacted by a predatory publisher — how do I find out?

Career Feature 15 MAY 24

How I fled bombed Aleppo to continue my career in science

Career Feature 08 MAY 24

Illuminating ‘the ugly side of science’: fresh incentives for reporting negative results

US halts funding to controversial virus-hunting group: what researchers think

News 16 MAY 24

Japan can embrace open science — but flexible approaches are key

Correspondence 07 MAY 24

US funders to tighten oversight of controversial ‘gain of function’ research

News 07 MAY 24

Mount Etna’s spectacular smoke rings and more — April’s best science images

News 03 MAY 24

Research Associate - Metabolism

Houston, Texas (US)

Baylor College of Medicine (BCM)

Postdoc Fellowships

Train with world-renowned cancer researchers at NIH? Consider joining the Center for Cancer Research (CCR) at the National Cancer Institute

Bethesda, Maryland

NIH National Cancer Institute (NCI)

Faculty Recruitment, Westlake University School of Medicine

Faculty positions are open at four distinct ranks: Assistant Professor, Associate Professor, Full Professor, and Chair Professor.

Hangzhou, Zhejiang, China

Westlake University

PhD/master's Candidate

PhD/master's Candidate    Graduate School of Frontier Science Initiative, Kanazawa University is seeking candidates for PhD and master's students i...

Kanazawa University

Senior Research Assistant in Human Immunology (wet lab)

Senior Research Scientist in Human Immunology, high-dimensional (40+) cytometry, ICS and automated robotic platforms.

Boston, Massachusetts (US)

Boston University Atomic Lab

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

How To Write An A-Grade Literature Review

3 straightforward steps (with examples) + free template.

By: Derek Jansen (MBA) | Expert Reviewed By: Dr. Eunice Rautenbach | October 2019

Quality research is about building onto the existing work of others , “standing on the shoulders of giants”, as Newton put it. The literature review chapter of your dissertation, thesis or research project is where you synthesise this prior work and lay the theoretical foundation for your own research.

Long story short, this chapter is a pretty big deal, which is why you want to make sure you get it right . In this post, I’ll show you exactly how to write a literature review in three straightforward steps, so you can conquer this vital chapter (the smart way).

Overview: The Literature Review Process

• Understanding the “ why “
• Finding the relevant literature
• Cataloguing and synthesising the information
• Outlining & writing up your literature review
• Example of a literature review

But first, the “why”…

Before we unpack how to write the literature review chapter, we’ve got to look at the why . To put it bluntly, if you don’t understand the function and purpose of the literature review process, there’s no way you can pull it off well. So, what exactly is the purpose of the literature review?

Well, there are (at least) four core functions:

• For you to gain an understanding (and demonstrate this understanding) of where the research is at currently, what the key arguments and disagreements are.
• For you to identify the gap(s) in the literature and then use this as justification for your own research topic.
• To help you build a conceptual framework for empirical testing (if applicable to your research topic).
• To inform your methodological choices and help you source tried and tested questionnaires (for interviews ) and measurement instruments (for surveys ).

Most students understand the first point but don’t give any thought to the rest. To get the most from the literature review process, you must keep all four points front of mind as you review the literature (more on this shortly), or you’ll land up with a wonky foundation.

Okay – with the why out the way, let’s move on to the how . As mentioned above, writing your literature review is a process, which I’ll break down into three steps:

• Finding the most suitable literature
• Understanding , distilling and organising the literature
• Planning and writing up your literature review chapter

Importantly, you must complete steps one and two before you start writing up your chapter. I know it’s very tempting, but don’t try to kill two birds with one stone and write as you read. You’ll invariably end up wasting huge amounts of time re-writing and re-shaping, or you’ll just land up with a disjointed, hard-to-digest mess . Instead, you need to read first and distil the information, then plan and execute the writing.

Step 1: Find the relevant literature

Naturally, the first step in the literature review journey is to hunt down the existing research that’s relevant to your topic. While you probably already have a decent base of this from your research proposal , you need to expand on this substantially in the dissertation or thesis itself.

Essentially, you need to be looking for any existing literature that potentially helps you answer your research question (or develop it, if that’s not yet pinned down). There are numerous ways to find relevant literature, but I’ll cover my top four tactics here. I’d suggest combining all four methods to ensure that nothing slips past you:

Method 1 – Google Scholar Scrubbing

Google’s academic search engine, Google Scholar , is a great starting point as it provides a good high-level view of the relevant journal articles for whatever keyword you throw at it. Most valuably, it tells you how many times each article has been cited, which gives you an idea of how credible (or at least, popular) it is. Some articles will be free to access, while others will require an account, which brings us to the next method.

Method 2 – University Database Scrounging

Generally, universities provide students with access to an online library, which provides access to many (but not all) of the major journals.

So, if you find an article using Google Scholar that requires paid access (which is quite likely), search for that article in your university’s database – if it’s listed there, you’ll have access. Note that, generally, the search engine capabilities of these databases are poor, so make sure you search for the exact article name, or you might not find it.

Method 3 – Journal Article Snowballing

At the end of every academic journal article, you’ll find a list of references. As with any academic writing, these references are the building blocks of the article, so if the article is relevant to your topic, there’s a good chance a portion of the referenced works will be too. Do a quick scan of the titles and see what seems relevant, then search for the relevant ones in your university’s database.

Method 4 – Dissertation Scavenging

Similar to Method 3 above, you can leverage other students’ dissertations. All you have to do is skim through literature review chapters of existing dissertations related to your topic and you’ll find a gold mine of potential literature. Usually, your university will provide you with access to previous students’ dissertations, but you can also find a much larger selection in the following databases:

• Open Access Theses & Dissertations
• Stanford SearchWorks

Keep in mind that dissertations and theses are not as academically sound as published, peer-reviewed journal articles (because they’re written by students, not professionals), so be sure to check the credibility of any sources you find using this method. You can do this by assessing the citation count of any given article in Google Scholar. If you need help with assessing the credibility of any article, or with finding relevant research in general, you can chat with one of our Research Specialists .

Alright – with a good base of literature firmly under your belt, it’s time to move onto the next step.

Need a helping hand?

Step 2: Log, catalogue and synthesise

Once you’ve built a little treasure trove of articles, it’s time to get reading and start digesting the information – what does it all mean?

While I present steps one and two (hunting and digesting) as sequential, in reality, it’s more of a back-and-forth tango – you’ll read a little , then have an idea, spot a new citation, or a new potential variable, and then go back to searching for articles. This is perfectly natural – through the reading process, your thoughts will develop , new avenues might crop up, and directional adjustments might arise. This is, after all, one of the main purposes of the literature review process (i.e. to familiarise yourself with the current state of research in your field).

As you’re working through your treasure chest, it’s essential that you simultaneously start organising the information. There are three aspects to this:

• Logging reference information
• Building an organised catalogue
• Distilling and synthesising the information

I’ll discuss each of these below:

2.1 – Log the reference information

As you read each article, you should add it to your reference management software. I usually recommend Mendeley for this purpose (see the Mendeley 101 video below), but you can use whichever software you’re comfortable with. Most importantly, make sure you load EVERY article you read into your reference manager, even if it doesn’t seem very relevant at the time.

2.2 – Build an organised catalogue

In the beginning, you might feel confident that you can remember who said what, where, and what their main arguments were. Trust me, you won’t. If you do a thorough review of the relevant literature (as you must!), you’re going to read many, many articles, and it’s simply impossible to remember who said what, when, and in what context . Also, without the bird’s eye view that a catalogue provides, you’ll miss connections between various articles, and have no view of how the research developed over time. Simply put, it’s essential to build your own catalogue of the literature.

I would suggest using Excel to build your catalogue, as it allows you to run filters, colour code and sort – all very useful when your list grows large (which it will). How you lay your spreadsheet out is up to you, but I’d suggest you have the following columns (at minimum):

• Author, date, title – Start with three columns containing this core information. This will make it easy for you to search for titles with certain words, order research by date, or group by author.
• Categories or keywords – You can either create multiple columns, one for each category/theme and then tick the relevant categories, or you can have one column with keywords.
• Key arguments/points – Use this column to succinctly convey the essence of the article, the key arguments and implications thereof for your research.
• Context – Note the socioeconomic context in which the research was undertaken. For example, US-based, respondents aged 25-35, lower- income, etc. This will be useful for making an argument about gaps in the research.
• Methodology – Note which methodology was used and why. Also, note any issues you feel arise due to the methodology. Again, you can use this to make an argument about gaps in the research.
• Quotations – Note down any quoteworthy lines you feel might be useful later.
• Notes – Make notes about anything not already covered. For example, linkages to or disagreements with other theories, questions raised but unanswered, shortcomings or limitations, and so forth.

If you’d like, you can try out our free catalog template here (see screenshot below).

2.3 – Digest and synthesise

Most importantly, as you work through the literature and build your catalogue, you need to synthesise all the information in your own mind – how does it all fit together? Look for links between the various articles and try to develop a bigger picture view of the state of the research. Some important questions to ask yourself are:

• What answers does the existing research provide to my own research questions ?
• Which points do the researchers agree (and disagree) on?
• How has the research developed over time?
• Where do the gaps in the current research lie?

To help you develop a big-picture view and synthesise all the information, you might find mind mapping software such as Freemind useful. Alternatively, if you’re a fan of physical note-taking, investing in a large whiteboard might work for you.

Step 3: Outline and write it up!

Once you’re satisfied that you have digested and distilled all the relevant literature in your mind, it’s time to put pen to paper (or rather, fingers to keyboard). There are two steps here – outlining and writing:

3.1 – Draw up your outline

Having spent so much time reading, it might be tempting to just start writing up without a clear structure in mind. However, it’s critically important to decide on your structure and develop a detailed outline before you write anything. Your literature review chapter needs to present a clear, logical and an easy to follow narrative – and that requires some planning. Don’t try to wing it!

Naturally, you won’t always follow the plan to the letter, but without a detailed outline, you’re more than likely going to end up with a disjointed pile of waffle , and then you’re going to spend a far greater amount of time re-writing, hacking and patching. The adage, “measure twice, cut once” is very suitable here.

In terms of structure, the first decision you’ll have to make is whether you’ll lay out your review thematically (into themes) or chronologically (by date/period). The right choice depends on your topic, research objectives and research questions, which we discuss in this article .

Once that’s decided, you need to draw up an outline of your entire chapter in bullet point format. Try to get as detailed as possible, so that you know exactly what you’ll cover where, how each section will connect to the next, and how your entire argument will develop throughout the chapter. Also, at this stage, it’s a good idea to allocate rough word count limits for each section, so that you can identify word count problems before you’ve spent weeks or months writing!

PS – check out our free literature review chapter template…

3.2 – Get writing

With a detailed outline at your side, it’s time to start writing up (finally!). At this stage, it’s common to feel a bit of writer’s block and find yourself procrastinating under the pressure of finally having to put something on paper. To help with this, remember that the objective of the first draft is not perfection – it’s simply to get your thoughts out of your head and onto paper, after which you can refine them. The structure might change a little, the word count allocations might shift and shuffle, and you might add or remove a section – that’s all okay. Don’t worry about all this on your first draft – just get your thoughts down on paper.

Once you’ve got a full first draft (however rough it may be), step away from it for a day or two (longer if you can) and then come back at it with fresh eyes. Pay particular attention to the flow and narrative – does it fall fit together and flow from one section to another smoothly? Now’s the time to try to improve the linkage from each section to the next, tighten up the writing to be more concise, trim down word count and sand it down into a more digestible read.

Once you’ve done that, give your writing to a friend or colleague who is not a subject matter expert and ask them if they understand the overall discussion. The best way to assess this is to ask them to explain the chapter back to you. This technique will give you a strong indication of which points were clearly communicated and which weren’t. If you’re working with Grad Coach, this is a good time to have your Research Specialist review your chapter.

Finally, tighten it up and send it off to your supervisor for comment. Some might argue that you should be sending your work to your supervisor sooner than this (indeed your university might formally require this), but in my experience, supervisors are extremely short on time (and often patience), so, the more refined your chapter is, the less time they’ll waste on addressing basic issues (which you know about already) and the more time they’ll spend on valuable feedback that will increase your mark-earning potential.

Literature Review Example

In the video below, we unpack an actual literature review so that you can see how all the core components come together in reality.

Let’s Recap

In this post, we’ve covered how to research and write up a high-quality literature review chapter. Let’s do a quick recap of the key takeaways:

• It is essential to understand the WHY of the literature review before you read or write anything. Make sure you understand the 4 core functions of the process.
• The first step is to hunt down the relevant literature . You can do this using Google Scholar, your university database, the snowballing technique and by reviewing other dissertations and theses.
• Next, you need to log all the articles in your reference manager , build your own catalogue of literature and synthesise all the research.
• Following that, you need to develop a detailed outline of your entire chapter – the more detail the better. Don’t start writing without a clear outline (on paper, not in your head!)
• Write up your first draft in rough form – don’t aim for perfection. Remember, done beats perfect.
• Refine your second draft and get a layman’s perspective on it . Then tighten it up and submit it to your supervisor.

Psst… there’s more!

This post is an extract from our bestselling short course, Literature Review Bootcamp . If you want to work smart, you don't want to miss this .

You Might Also Like:

38 Comments

Thank you very much. This page is an eye opener and easy to comprehend.

This is awesome!

I wish I come across GradCoach earlier enough.

But all the same I’ll make use of this opportunity to the fullest.

Thank you for this good job.

Keep it up!

You’re welcome, Yinka. Thank you for the kind words. All the best writing your literature review.

Thank you for a very useful literature review session. Although I am doing most of the steps…it being my first masters an Mphil is a self study and one not sure you are on the right track. I have an amazing supervisor but one also knows they are super busy. So not wanting to bother on the minutae. Thank you.

You’re most welcome, Renee. Good luck with your literature review 🙂

This has been really helpful. Will make full use of it. 🙂

Thank you Gradcoach.

Really agreed. Admirable effort

thank you for this beautiful well explained recap.

Thank you so much for your guide of video and other instructions for the dissertation writing.

It is instrumental. It encouraged me to write a dissertation now.

Thank you the video was great – from someone that knows nothing thankyou

an amazing and very constructive way of presetting a topic, very useful, thanks for the effort,

It is timely

It is very good video of guidance for writing a research proposal and a dissertation. Since I have been watching and reading instructions, I have started my research proposal to write. I appreciate to Mr Jansen hugely.

I learn a lot from your videos. Very comprehensive and detailed.

Thank you for sharing your knowledge. As a research student, you learn better with your learning tips in research

I was really stuck in reading and gathering information but after watching these things are cleared thanks, it is so helpful.

Really helpful, Thank you for the effort in showing such information

This is super helpful thank you very much.

Thank you for this whole literature writing review.You have simplified the process.

I’m so glad I found GradCoach. Excellent information, Clear explanation, and Easy to follow, Many thanks Derek!

You’re welcome, Maithe. Good luck writing your literature review 🙂

Thank you Coach, you have greatly enriched and improved my knowledge

Great piece, so enriching and it is going to help me a great lot in my project and thesis, thanks so much

This is THE BEST site for ANYONE doing a masters or doctorate! Thank you for the sound advice and templates. You rock!

Thanks, Stephanie 🙂

This is mind blowing, the detailed explanation and simplicity is perfect.

I am doing two papers on my final year thesis, and I must stay I feel very confident to face both headlong after reading this article.

thank you so much.

if anyone is to get a paper done on time and in the best way possible, GRADCOACH is certainly the go to area!

This is very good video which is well explained with detailed explanation

Thank you excellent piece of work and great mentoring

Thanks, it was useful

Thank you very much. the video and the information were very helpful.

Good morning scholar. I’m delighted coming to know you even before the commencement of my dissertation which hopefully is expected in not more than six months from now. I would love to engage my study under your guidance from the beginning to the end. I love to know how to do good job

Thank you so much Derek for such useful information on writing up a good literature review. I am at a stage where I need to start writing my one. My proposal was accepted late last year but I honestly did not know where to start

Like the name of your YouTube implies you are GRAD (great,resource person, about dissertation). In short you are smart enough in coaching research work.

This is a very well thought out webpage. Very informative and a great read.

Very timely.

I appreciate.

Very comprehensive and eye opener for me as beginner in postgraduate study. Well explained and easy to understand. Appreciate and good reference in guiding me in my research journey. Thank you

Thank you. I requested to download the free literature review template, however, your website wouldn’t allow me to complete the request or complete a download. May I request that you email me the free template? Thank you.

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

• UWF Libraries

Literature Review: Conducting & Writing

• Sample Literature Reviews
• Steps for Conducting a Lit Review
• Finding "The Literature"
• Organizing/Writing
• APA Style This link opens in a new window
• Chicago: Notes Bibliography This link opens in a new window
• MLA Style This link opens in a new window

Sample Lit Reviews from Communication Arts

Have an exemplary literature review.

• Literature Review Sample 1
• Literature Review Sample 2
• Literature Review Sample 3

Have you written a stellar literature review you care to share for teaching purposes?

Are you an instructor who has received an exemplary literature review and have permission from the student to post?

Please contact Britt McGowan at [email protected] for inclusion in this guide. All disciplines welcome and encouraged.

• << Previous: MLA Style
• Next: Get Help! >>
• Last Updated: Mar 22, 2024 9:37 AM
• URL: https://libguides.uwf.edu/litreview

How do I Write a Literature Review?: #5 Writing the Review

• Step #1: Choosing a Topic
• Step #2: Finding Information
• Step #3: Evaluating Content
• Step #4: Synthesizing Content
• #5 Writing the Review
• Citing Your Sources

WRITING THE REVIEW 

You've done the research and now you're ready to put your findings down on paper. When preparing to write your review, first consider how will you organize your review.

The actual review generally has 5 components:

Abstract  -  An abstract is a summary of your literature review. It is made up of the following parts:

• A contextual sentence about your motivation behind your research topic
• Your thesis statement
• A descriptive statement about the types of literature used in the review
• Summarize your findings
• Conclusion(s) based upon your findings

Introduction :   Like a typical research paper introduction, provide the reader with a quick idea of the topic of the literature review:

• Define or identify the general topic, issue, or area of concern. This provides the reader with context for reviewing the literature.
• Identify related trends in what has already been published about the topic; or conflicts in theory, methodology, evidence, and conclusions; or gaps in research and scholarship; or a single problem or new perspective of immediate interest.
• Establish your reason (point of view) for reviewing the literature; explain the criteria to be used in analyzing and comparing literature and the organization of the review (sequence); and, when necessary, state why certain literature is or is not included (scope)  - 

Body :  The body of a literature review contains your discussion of sources and can be organized in 3 ways-

• Chronological -  by publication or by trend
• Thematic -  organized around a topic or issue, rather than the progression of time
• Methodical -  the focusing factor usually does not have to do with the content of the material. Instead, it focuses on the "methods" of the literature's researcher or writer that you are reviewing

You may also want to include a section on "questions for further research" and discuss what questions the review has sparked about the topic/field or offer suggestions for future studies/examinations that build on your current findings.

Conclusion :  In the conclusion, you should:

Conclude your paper by providing your reader with some perspective on the relationship between your literature review's specific topic and how it's related to it's parent discipline, scientific endeavor, or profession.

Bibliography :   Since a literature review is composed of pieces of research, it is very important that your correctly cite the literature you are reviewing, both in the reviews body as well as in a bibliography/works cited. To learn more about different citation styles, visit the " Citing Your Sources " tab.

• Writing a Literature Review: Wesleyan University
• Literature Review: Edith Cowan University
• << Previous: Step #4: Synthesizing Content
• Next: Citing Your Sources >>
• Last Updated: Aug 22, 2023 1:35 PM
• URL: https://libguides.eastern.edu/literature_reviews

About the Library

• Collection Development
• Circulation Policies
• Mission Statement
• Staff Directory

Using the Library

• A to Z Journal List
• Library Catalog
• Research Guides

Interlibrary Services

• Research Help

Warner Memorial Library

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• 5. The Literature Review
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

A literature review surveys prior research published in books, scholarly articles, and any other sources relevant to a particular issue, area of research, or theory, and by so doing, provides a description, summary, and critical evaluation of these works in relation to the research problem being investigated. Literature reviews are designed to provide an overview of sources you have used in researching a particular topic and to demonstrate to your readers how your research fits within existing scholarship about the topic.

Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper . Fourth edition. Thousand Oaks, CA: SAGE, 2014.

Importance of a Good Literature Review

A literature review may consist of simply a summary of key sources, but in the social sciences, a literature review usually has an organizational pattern and combines both summary and synthesis, often within specific conceptual categories . A summary is a recap of the important information of the source, but a synthesis is a re-organization, or a reshuffling, of that information in a way that informs how you are planning to investigate a research problem. The analytical features of a literature review might:

• Give a new interpretation of old material or combine new with old interpretations,
• Trace the intellectual progression of the field, including major debates,
• Depending on the situation, evaluate the sources and advise the reader on the most pertinent or relevant research, or
• Usually in the conclusion of a literature review, identify where gaps exist in how a problem has been researched to date.

Given this, the purpose of a literature review is to:

• Place each work in the context of its contribution to understanding the research problem being studied.
• Describe the relationship of each work to the others under consideration.
• Identify new ways to interpret prior research.
• Reveal any gaps that exist in the literature.
• Resolve conflicts amongst seemingly contradictory previous studies.
• Identify areas of prior scholarship to prevent duplication of effort.
• Point the way in fulfilling a need for additional research.
• Locate your own research within the context of existing literature [very important].

Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper. 2nd ed. Thousand Oaks, CA: Sage, 2005; Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1998; Jesson, Jill. Doing Your Literature Review: Traditional and Systematic Techniques . Los Angeles, CA: SAGE, 2011; Knopf, Jeffrey W. "Doing a Literature Review." PS: Political Science and Politics 39 (January 2006): 127-132; Ridley, Diana. The Literature Review: A Step-by-Step Guide for Students . 2nd ed. Los Angeles, CA: SAGE, 2012.

Types of Literature Reviews

It is important to think of knowledge in a given field as consisting of three layers. First, there are the primary studies that researchers conduct and publish. Second are the reviews of those studies that summarize and offer new interpretations built from and often extending beyond the primary studies. Third, there are the perceptions, conclusions, opinion, and interpretations that are shared informally among scholars that become part of the body of epistemological traditions within the field.

In composing a literature review, it is important to note that it is often this third layer of knowledge that is cited as "true" even though it often has only a loose relationship to the primary studies and secondary literature reviews. Given this, while literature reviews are designed to provide an overview and synthesis of pertinent sources you have explored, there are a number of approaches you could adopt depending upon the type of analysis underpinning your study.

Argumentative Review This form examines literature selectively in order to support or refute an argument, deeply embedded assumption, or philosophical problem already established in the literature. The purpose is to develop a body of literature that establishes a contrarian viewpoint. Given the value-laden nature of some social science research [e.g., educational reform; immigration control], argumentative approaches to analyzing the literature can be a legitimate and important form of discourse. However, note that they can also introduce problems of bias when they are used to make summary claims of the sort found in systematic reviews [see below].

Integrative Review Considered a form of research that reviews, critiques, and synthesizes representative literature on a topic in an integrated way such that new frameworks and perspectives on the topic are generated. The body of literature includes all studies that address related or identical hypotheses or research problems. A well-done integrative review meets the same standards as primary research in regard to clarity, rigor, and replication. This is the most common form of review in the social sciences.

Historical Review Few things rest in isolation from historical precedent. Historical literature reviews focus on examining research throughout a period of time, often starting with the first time an issue, concept, theory, phenomena emerged in the literature, then tracing its evolution within the scholarship of a discipline. The purpose is to place research in a historical context to show familiarity with state-of-the-art developments and to identify the likely directions for future research.

Methodological Review A review does not always focus on what someone said [findings], but how they came about saying what they say [method of analysis]. Reviewing methods of analysis provides a framework of understanding at different levels [i.e. those of theory, substantive fields, research approaches, and data collection and analysis techniques], how researchers draw upon a wide variety of knowledge ranging from the conceptual level to practical documents for use in fieldwork in the areas of ontological and epistemological consideration, quantitative and qualitative integration, sampling, interviewing, data collection, and data analysis. This approach helps highlight ethical issues which you should be aware of and consider as you go through your own study.

Systematic Review This form consists of an overview of existing evidence pertinent to a clearly formulated research question, which uses pre-specified and standardized methods to identify and critically appraise relevant research, and to collect, report, and analyze data from the studies that are included in the review. The goal is to deliberately document, critically evaluate, and summarize scientifically all of the research about a clearly defined research problem . Typically it focuses on a very specific empirical question, often posed in a cause-and-effect form, such as "To what extent does A contribute to B?" This type of literature review is primarily applied to examining prior research studies in clinical medicine and allied health fields, but it is increasingly being used in the social sciences.

Theoretical Review The purpose of this form is to examine the corpus of theory that has accumulated in regard to an issue, concept, theory, phenomena. The theoretical literature review helps to establish what theories already exist, the relationships between them, to what degree the existing theories have been investigated, and to develop new hypotheses to be tested. Often this form is used to help establish a lack of appropriate theories or reveal that current theories are inadequate for explaining new or emerging research problems. The unit of analysis can focus on a theoretical concept or a whole theory or framework.

NOTE : Most often the literature review will incorporate some combination of types. For example, a review that examines literature supporting or refuting an argument, assumption, or philosophical problem related to the research problem will also need to include writing supported by sources that establish the history of these arguments in the literature.

Baumeister, Roy F. and Mark R. Leary. "Writing Narrative Literature Reviews."  Review of General Psychology 1 (September 1997): 311-320; Mark R. Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper . 2nd ed. Thousand Oaks, CA: Sage, 2005; Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1998; Kennedy, Mary M. "Defining a Literature." Educational Researcher 36 (April 2007): 139-147; Petticrew, Mark and Helen Roberts. Systematic Reviews in the Social Sciences: A Practical Guide . Malden, MA: Blackwell Publishers, 2006; Torracro, Richard. "Writing Integrative Literature Reviews: Guidelines and Examples." Human Resource Development Review 4 (September 2005): 356-367; Rocco, Tonette S. and Maria S. Plakhotnik. "Literature Reviews, Conceptual Frameworks, and Theoretical Frameworks: Terms, Functions, and Distinctions." Human Ressource Development Review 8 (March 2008): 120-130; Sutton, Anthea. Systematic Approaches to a Successful Literature Review . Los Angeles, CA: Sage Publications, 2016.

Structure and Writing Style

I.  Thinking About Your Literature Review

The structure of a literature review should include the following in support of understanding the research problem :

• An overview of the subject, issue, or theory under consideration, along with the objectives of the literature review,
• Division of works under review into themes or categories [e.g. works that support a particular position, those against, and those offering alternative approaches entirely],
• An explanation of how each work is similar to and how it varies from the others,
• Conclusions as to which pieces are best considered in their argument, are most convincing of their opinions, and make the greatest contribution to the understanding and development of their area of research.

The critical evaluation of each work should consider :

• Provenance -- what are the author's credentials? Are the author's arguments supported by evidence [e.g. primary historical material, case studies, narratives, statistics, recent scientific findings]?
• Methodology -- were the techniques used to identify, gather, and analyze the data appropriate to addressing the research problem? Was the sample size appropriate? Were the results effectively interpreted and reported?
• Objectivity -- is the author's perspective even-handed or prejudicial? Is contrary data considered or is certain pertinent information ignored to prove the author's point?
• Persuasiveness -- which of the author's theses are most convincing or least convincing?
• Validity -- are the author's arguments and conclusions convincing? Does the work ultimately contribute in any significant way to an understanding of the subject?

II.  Development of the Literature Review

Four Basic Stages of Writing 1.  Problem formulation -- which topic or field is being examined and what are its component issues? 2.  Literature search -- finding materials relevant to the subject being explored. 3.  Data evaluation -- determining which literature makes a significant contribution to the understanding of the topic. 4.  Analysis and interpretation -- discussing the findings and conclusions of pertinent literature.

Consider the following issues before writing the literature review: Clarify If your assignment is not specific about what form your literature review should take, seek clarification from your professor by asking these questions: 1.  Roughly how many sources would be appropriate to include? 2.  What types of sources should I review (books, journal articles, websites; scholarly versus popular sources)? 3.  Should I summarize, synthesize, or critique sources by discussing a common theme or issue? 4.  Should I evaluate the sources in any way beyond evaluating how they relate to understanding the research problem? 5.  Should I provide subheadings and other background information, such as definitions and/or a history? Find Models Use the exercise of reviewing the literature to examine how authors in your discipline or area of interest have composed their literature review sections. Read them to get a sense of the types of themes you might want to look for in your own research or to identify ways to organize your final review. The bibliography or reference section of sources you've already read, such as required readings in the course syllabus, are also excellent entry points into your own research. Narrow the Topic The narrower your topic, the easier it will be to limit the number of sources you need to read in order to obtain a good survey of relevant resources. Your professor will probably not expect you to read everything that's available about the topic, but you'll make the act of reviewing easier if you first limit scope of the research problem. A good strategy is to begin by searching the USC Libraries Catalog for recent books about the topic and review the table of contents for chapters that focuses on specific issues. You can also review the indexes of books to find references to specific issues that can serve as the focus of your research. For example, a book surveying the history of the Israeli-Palestinian conflict may include a chapter on the role Egypt has played in mediating the conflict, or look in the index for the pages where Egypt is mentioned in the text. Consider Whether Your Sources are Current Some disciplines require that you use information that is as current as possible. This is particularly true in disciplines in medicine and the sciences where research conducted becomes obsolete very quickly as new discoveries are made. However, when writing a review in the social sciences, a survey of the history of the literature may be required. In other words, a complete understanding the research problem requires you to deliberately examine how knowledge and perspectives have changed over time. Sort through other current bibliographies or literature reviews in the field to get a sense of what your discipline expects. You can also use this method to explore what is considered by scholars to be a "hot topic" and what is not.

III.  Ways to Organize Your Literature Review

Chronology of Events If your review follows the chronological method, you could write about the materials according to when they were published. This approach should only be followed if a clear path of research building on previous research can be identified and that these trends follow a clear chronological order of development. For example, a literature review that focuses on continuing research about the emergence of German economic power after the fall of the Soviet Union. By Publication Order your sources by publication chronology, then, only if the order demonstrates a more important trend. For instance, you could order a review of literature on environmental studies of brown fields if the progression revealed, for example, a change in the soil collection practices of the researchers who wrote and/or conducted the studies. Thematic [“conceptual categories”] A thematic literature review is the most common approach to summarizing prior research in the social and behavioral sciences. Thematic reviews are organized around a topic or issue, rather than the progression of time, although the progression of time may still be incorporated into a thematic review. For example, a review of the Internet’s impact on American presidential politics could focus on the development of online political satire. While the study focuses on one topic, the Internet’s impact on American presidential politics, it would still be organized chronologically reflecting technological developments in media. The difference in this example between a "chronological" and a "thematic" approach is what is emphasized the most: themes related to the role of the Internet in presidential politics. Note that more authentic thematic reviews tend to break away from chronological order. A review organized in this manner would shift between time periods within each section according to the point being made. Methodological A methodological approach focuses on the methods utilized by the researcher. For the Internet in American presidential politics project, one methodological approach would be to look at cultural differences between the portrayal of American presidents on American, British, and French websites. Or the review might focus on the fundraising impact of the Internet on a particular political party. A methodological scope will influence either the types of documents in the review or the way in which these documents are discussed.

Other Sections of Your Literature Review Once you've decided on the organizational method for your literature review, the sections you need to include in the paper should be easy to figure out because they arise from your organizational strategy. In other words, a chronological review would have subsections for each vital time period; a thematic review would have subtopics based upon factors that relate to the theme or issue. However, sometimes you may need to add additional sections that are necessary for your study, but do not fit in the organizational strategy of the body. What other sections you include in the body is up to you. However, only include what is necessary for the reader to locate your study within the larger scholarship about the research problem.

Here are examples of other sections, usually in the form of a single paragraph, you may need to include depending on the type of review you write:

• Current Situation : Information necessary to understand the current topic or focus of the literature review.
• Sources Used : Describes the methods and resources [e.g., databases] you used to identify the literature you reviewed.
• History : The chronological progression of the field, the research literature, or an idea that is necessary to understand the literature review, if the body of the literature review is not already a chronology.
• Selection Methods : Criteria you used to select (and perhaps exclude) sources in your literature review. For instance, you might explain that your review includes only peer-reviewed [i.e., scholarly] sources.
• Standards : Description of the way in which you present your information.
• Questions for Further Research : What questions about the field has the review sparked? How will you further your research as a result of the review?

IV.  Writing Your Literature Review

Once you've settled on how to organize your literature review, you're ready to write each section. When writing your review, keep in mind these issues.

Use Evidence A literature review section is, in this sense, just like any other academic research paper. Your interpretation of the available sources must be backed up with evidence [citations] that demonstrates that what you are saying is valid. Be Selective Select only the most important points in each source to highlight in the review. The type of information you choose to mention should relate directly to the research problem, whether it is thematic, methodological, or chronological. Related items that provide additional information, but that are not key to understanding the research problem, can be included in a list of further readings . Use Quotes Sparingly Some short quotes are appropriate if you want to emphasize a point, or if what an author stated cannot be easily paraphrased. Sometimes you may need to quote certain terminology that was coined by the author, is not common knowledge, or taken directly from the study. Do not use extensive quotes as a substitute for using your own words in reviewing the literature. Summarize and Synthesize Remember to summarize and synthesize your sources within each thematic paragraph as well as throughout the review. Recapitulate important features of a research study, but then synthesize it by rephrasing the study's significance and relating it to your own work and the work of others. Keep Your Own Voice While the literature review presents others' ideas, your voice [the writer's] should remain front and center. For example, weave references to other sources into what you are writing but maintain your own voice by starting and ending the paragraph with your own ideas and wording. Use Caution When Paraphrasing When paraphrasing a source that is not your own, be sure to represent the author's information or opinions accurately and in your own words. Even when paraphrasing an author’s work, you still must provide a citation to that work.

V.  Common Mistakes to Avoid

These are the most common mistakes made in reviewing social science research literature.

• Sources in your literature review do not clearly relate to the research problem;
• You do not take sufficient time to define and identify the most relevant sources to use in the literature review related to the research problem;
• Relies exclusively on secondary analytical sources rather than including relevant primary research studies or data;
• Uncritically accepts another researcher's findings and interpretations as valid, rather than examining critically all aspects of the research design and analysis;
• Does not describe the search procedures that were used in identifying the literature to review;
• Reports isolated statistical results rather than synthesizing them in chi-squared or meta-analytic methods; and,
• Only includes research that validates assumptions and does not consider contrary findings and alternative interpretations found in the literature.

Cook, Kathleen E. and Elise Murowchick. “Do Literature Review Skills Transfer from One Course to Another?” Psychology Learning and Teaching 13 (March 2014): 3-11; Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper . 2nd ed. Thousand Oaks, CA: Sage, 2005; Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1998; Jesson, Jill. Doing Your Literature Review: Traditional and Systematic Techniques . London: SAGE, 2011; Literature Review Handout. Online Writing Center. Liberty University; Literature Reviews. The Writing Center. University of North Carolina; Onwuegbuzie, Anthony J. and Rebecca Frels. Seven Steps to a Comprehensive Literature Review: A Multimodal and Cultural Approach . Los Angeles, CA: SAGE, 2016; Ridley, Diana. The Literature Review: A Step-by-Step Guide for Students . 2nd ed. Los Angeles, CA: SAGE, 2012; Randolph, Justus J. “A Guide to Writing the Dissertation Literature Review." Practical Assessment, Research, and Evaluation. vol. 14, June 2009; Sutton, Anthea. Systematic Approaches to a Successful Literature Review . Los Angeles, CA: Sage Publications, 2016; Taylor, Dena. The Literature Review: A Few Tips On Conducting It. University College Writing Centre. University of Toronto; Writing a Literature Review. Academic Skills Centre. University of Canberra.

Writing Tip

Break Out of Your Disciplinary Box!

Thinking interdisciplinarily about a research problem can be a rewarding exercise in applying new ideas, theories, or concepts to an old problem. For example, what might cultural anthropologists say about the continuing conflict in the Middle East? In what ways might geographers view the need for better distribution of social service agencies in large cities than how social workers might study the issue? You don’t want to substitute a thorough review of core research literature in your discipline for studies conducted in other fields of study. However, particularly in the social sciences, thinking about research problems from multiple vectors is a key strategy for finding new solutions to a problem or gaining a new perspective. Consult with a librarian about identifying research databases in other disciplines; almost every field of study has at least one comprehensive database devoted to indexing its research literature.

Frodeman, Robert. The Oxford Handbook of Interdisciplinarity . New York: Oxford University Press, 2010.

Another Writing Tip

Don't Just Review for Content!

While conducting a review of the literature, maximize the time you devote to writing this part of your paper by thinking broadly about what you should be looking for and evaluating. Review not just what scholars are saying, but how are they saying it. Some questions to ask:

• How are they organizing their ideas?
• What methods have they used to study the problem?
• What theories have been used to explain, predict, or understand their research problem?
• What sources have they cited to support their conclusions?
• How have they used non-textual elements [e.g., charts, graphs, figures, etc.] to illustrate key points?

When you begin to write your literature review section, you'll be glad you dug deeper into how the research was designed and constructed because it establishes a means for developing more substantial analysis and interpretation of the research problem.

Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1 998.

Yet Another Writing Tip

When Do I Know I Can Stop Looking and Move On?

Here are several strategies you can utilize to assess whether you've thoroughly reviewed the literature:

• Look for repeating patterns in the research findings . If the same thing is being said, just by different people, then this likely demonstrates that the research problem has hit a conceptual dead end. At this point consider: Does your study extend current research?  Does it forge a new path? Or, does is merely add more of the same thing being said?
• Look at sources the authors cite to in their work . If you begin to see the same researchers cited again and again, then this is often an indication that no new ideas have been generated to address the research problem.
• Search Google Scholar to identify who has subsequently cited leading scholars already identified in your literature review [see next sub-tab]. This is called citation tracking and there are a number of sources that can help you identify who has cited whom, particularly scholars from outside of your discipline. Here again, if the same authors are being cited again and again, this may indicate no new literature has been written on the topic.

Onwuegbuzie, Anthony J. and Rebecca Frels. Seven Steps to a Comprehensive Literature Review: A Multimodal and Cultural Approach . Los Angeles, CA: Sage, 2016; Sutton, Anthea. Systematic Approaches to a Successful Literature Review . Los Angeles, CA: Sage Publications, 2016.

• << Previous: Theoretical Framework
• Next: Citation Tracking >>
• Last Updated: May 15, 2024 9:53 AM
• URL: https://libguides.usc.edu/writingguide

• Open access
• Published: 13 May 2024

Neighborhood based computational approaches for the prediction of lncRNA-disease associations

• Mariella Bonomo 1 &
• Simona E. Rombo 1 , 2  

BMC Bioinformatics volume  25 , Article number:  187 ( 2024 ) Cite this article

106 Accesses

Metrics details

Long non-coding RNAs (lncRNAs) are a class of molecules involved in important biological processes. Extensive efforts have been provided to get deeper understanding of disease mechanisms at the lncRNA level, guiding towards the detection of biomarkers for disease diagnosis, treatment, prognosis and prevention. Unfortunately, due to costs and time complexity, the number of possible disease-related lncRNAs verified by traditional biological experiments is very limited. Computational approaches for the prediction of disease-lncRNA associations allow to identify the most promising candidates to be verified in laboratory, reducing costs and time consuming.

We propose novel approaches for the prediction of lncRNA-disease associations, all sharing the idea of exploring associations among lncRNAs, other intermediate molecules (e.g., miRNAs) and diseases, suitably represented by tripartite graphs. Indeed, while only a few lncRNA-disease associations are still known, plenty of interactions between lncRNAs and other molecules, as well as associations of the latters with diseases, are available. A first approach presented here, NGH, relies on neighborhood analysis performed on a tripartite graph, built upon lncRNAs, miRNAs and diseases. A second approach (CF) relies on collaborative filtering; a third approach (NGH-CF) is obtained boosting NGH by collaborative filtering. The proposed approaches have been validated on both synthetic and real data, and compared against other methods from the literature. It results that neighborhood analysis allows to outperform competitors, and when it is combined with collaborative filtering the prediction accuracy further improves, scoring a value of AUC equal to 0966.

Availability

Source code and sample datasets are available at: https://github.com/marybonomo/LDAsPredictionApproaches.git

Peer Review reports

Introduction

More than \(98\%\) of the human genome consists of non-coding regions, considered in the past as “junk” DNA. However, in the last decades evidence has been shown that non-coding genome elements often play an important role in regulating various critical biological processes [ 1 ]. An important class of non-coding molecules which have started to receive great attention in the last few years is represented by long non-coding RNAs (lncRNAs), that is, RNAs not translated into functional proteins, and longer than 200 nucleotides.

LncRNAs have been found to interplay with other molecules in order to perform important biological tasks, such as modulating chromatin function, regulating the assembly and function of membraneless nuclear bodies, interfering with signalling pathways [ 2 , 3 ]. Many of these functions ultimately affect gene expression in diverse biological and physiopathological contexts, such as in neuronal disorders, immune responses and cancer. Therefore, the alteration and dysregulation of lncRNAs have been associated with the occurrence and progress of many complex diseases [ 4 ].

The discovery of novel lncRNA-disease associations (LDAs) may provide valuable input to the understanding of disease mechanisms at lncRNA level, as well as to the detection of disease biomarkers for disease diagnosis, treatment, prognosis and prevention. Unfortunately, verifying that a specific lncRNA may have a role in the occurrence/progress of a given disease is an expensive process, therefore the number of disease-related lncRNAs verified by traditional biological experiments is yet very limited. Computational approaches for the prediction of potential LDAs can effectively decrease the time and cost of biological experiments, allowing for the identification of the most promising lncRNA-disease pairs to be further verified in laboratory (see [ 5 ] for a comprehensive review on the topic). Such approaches often train predictive models on the basis of the known and experimentally validated lncRNA-disease pairs (e.g., [ 6 , 7 , 8 , 9 ]). In other cases, they rely on the analysis of lncRNAs related information stored in public databases, such as their interaction with other types of molecules (e.g., [ 10 , 11 , 12 , 13 , 14 , 15 ]). As an example, large amounts of lncRNA-miRNA interactions have been collected in public databases, and plenty of experimentally confirmed miRNA-disease associations are available as well. However, although non-coding RNA function and its association with human complex diseases have been widely studied in the literature (see [ 16 , 17 , 18 ]), how to provide biologists with more accurate and ready-to-use software tools for LDAs prediction is yet an open challenge, due to the specific characteristics of lncRNAs (e.g., they are much less characterized than other non-coding RNAs.)

We propose three novel computational approaches for the prediction of LDAs, relying on the use of known lncRNA-miRNA interactions (LMIs) and miRNA-disease associations (MDAs). In particular, we model the problem of LDAs prediction as a neighborhood analysis performed on tripartite graphs, where the three sets of vertices represent lncRNAs, miRNAs and diseases, respectively, and vertices are linked according to LMIs and MDAs. Based on the assumption that similar lncRNAs interact with similar diseases [ 12 ], the first approach proposed here (NGH) aims at identifying novel LDAs by analyzing the behaviour of lncRNAs which are neighbors , in terms of their intermediate relationships with miRNAs. The main idea here is that neighborhood analysis automatically guides towards the detection of similar behaviours, and without the need of using a-priory known LDAs for training. Therefore, differently than other approaches from the literature, those proposed here do not involve verified LDAs in the prediction step, thus avoiding possible biases due to the fact that the number and variety of verified LDAs is yet very limited. The second presented approach (CF) relies on collaborative filtering, applied on the basis of common miRNAs shared by different lncRNAs. We have also explored the combination of neighborhood analysis with collaborative filtering, showing that this notably improves the LDAs prediction accuracy. Indeed, the third approach we have designed (NGH-CF) boosts NGH with collaborative filtering, and it is the best performing one, although also NGH and CF have been able to reach high accuracy values across all the different considered validation tests. In particular, Fig.  1 summarizes the research flowchart explained above.

Flowchart of the research pipeline. The miRNA-lncRNA interactions and miRNA-disease associations are exploited for the construction of the tripartite graph. The tripartite graph, in its turn, is at the basis of both neighborhood analysis and collaborative filtering steps, from which the three proposed approaches are obtained: NGH from neighborhood analysis, CF from collaborative filtering, NGH-CF from the combination of the two ones. Each prediction approach returns in output a LDAs rank

The proposed approaches have been exhaustively validated on both synthetic and real datasets, and the result is that they outperform (also significantly) the other methods from the literature. The experimental analysis shows that the improvement in accuracy achieved by the methods proposed here is due to their ability in capturing specific situations neglected by competitors. Examples of that are represented by true LDAs, detected by our approaches and not by the other approaches in the literature, where the involved lncRNA does not present intermediate molecules in common with the associated disease, although its neighbor lncRNAs share a large number of miRNAs with that disease. Moreover, it is shown that our approaches are robust to noise obtained by perturbing a controlled percentage of lncRNA-miRNA interactions and miRNA-disease associations, with NGH-CF the best one also for robustness. The obtained experimental results show that the prediction methods proposed here may effectively support biologists in selecting significant associations to be further verified in laboratory.

Novel putative LDAs coming from the consensus of the three proposed methods, and not yet registered in the available databases as experimentally verified, are provided. Interestingly, the core of novel LDAs returned with highest score by all three approaches finds evidence in the recent literature, while many other high scored predicted LDAs involve less studied lncRNAs, thus providing useful insights for their better characterization.

A first group of approaches aim at using existing true validated cases to train the prediction system, in order to make it able to correctly detect novel cases.

In [ 19 ] a Laplacian Regularized Least Squares is proposed to infer candidates LDAs ( LRLSLDA ) by applying a semi-supervised learning framework. LRLSLDA assumes that similar diseases tend to correlate with functionally similar lncRNAs, and vice versa. Thus, known LDAs and lncRNA expression profiles are combined to prioritize disease-associated lncRNA candidates by LRLSLDA, which does not require negative samples (i.e., confirmed uncorrelated LDAs). In [ 20 ] the method SKF-LDA is proposed that constructs a lncRNA-disease correlation matrix, based on the known LDAs. Then, it calculates the similarity between lncRNAs and that between diseases, according to specific metrics, and integrates such data. Finally, a predicted LDA matrix is obtained by the Laplacian Regularized Least Squares method. The method ENCFLDA [ 6 ] combines matrix decomposition and collaborative filtering. It uses matrix factorization combined with elastic networks and a collaborative filtering algorithm, making the prediction model more stable and eliminating the problem of data over-fitting. HGNNLDA recently proposed in [ 21 ] is based on hypergraph neural network, where the associations are modeled as a lncRNA-drug bipartite graph to build lncRNA hypergraph and drug hypergraph. Hypergraph convolution is then used to learn correlation of higher-order neighbors from the lncRNA and drug hypergraphs. LDAI-ISPS proposed in [ 22 ] is a LDAs inference approach based on space projections of integrated networks, recostructing the disease (lncRNA) integrated similarities network via integrating multiple information, such as disease semantic similarities, lncRNA functional similarities, and known LDAs. A space projection score is finally obtained via vector projections of the weighted networks. In [ 7 ] a consensual prediction approach called HOPEXGB is presented, to identify disease-related miRNAs and lncRNAs by high-order proximity preserved embedding and extreme gradient boosting. The authors build a heterogeneous disease-miRNA-lncRNA (DML) information network by linking lncRNA, miRNA, and disease nodes based on their correlation, and generate a negative dataset based on the similarities between unknown and known associations, in order to reduce the false negative rate in the data set for model construction. The method MAGCNSE proposed in [ 23 ] builds multiple feature matrices based on semantic similarity and disease Gaussian interaction profile kernel similarity of both lncRNAs and diseases. MAGCNSE adaptively assigns weights to the different feature matrices built upon the lncRNAs and diseases similarities. Then, it uses a convolutional neural network to further extract features from multi-channel feature matrices, in order to obtain the final representations of lncRNAs and diseases that is used for the LDAs prediction task.

LDAFGAN [ 8 ] is a model designed for predicting associations between long non-coding RNAs (lncRNAs) and diseases. This method is based on a generative and a discriminative networks, typically implemented as multilayer fully connected neural networks, which generate synthetic data based on some underlying distribution. The generative and discriminative networks are trained together in an adversarial manner. The generative network tries to generate realistic representations of lncRNA-disease associations, while the discriminative network tries to distinguish between real and fake associations. This adversarial training process helps the generative network learn to generate more realistic associations. Once the model is trained, it can predict associations between new lncRNAs and diseases without requiring associated data for those specific lncRNAs. The model captures the data distribution during training, which enables it to make predictions even for unseen lncRNAs. The approach GCNFORMER [ 9 ] is based on graph convolutional network and transformer. First, it integrates the intraclass similarity and interclass connections between miRNAs, lncRNAs and diseases, building a graph adjacency matrix. Then, the method extracts the features between various nodes, by a graph convolutional network. To obtain the global dependencies between inputs and outputs, a transformer encoder with a multiheaded attention mechanism to forecast lncRNA-disease associations is finally applied.

As for the approaches summarized above, it is worth to point out that they may suffer of the fact that the experimentally verified LDAs are still very limited, therefore the training set may be rather incomplete and not enough diversified. For this reason, when such approaches are applied for de novo LDAs prediction, their performance may drastically go down [ 12 ].

Other approaches from the literature use intermediate molecules (e.g., miRNA) to infer novel LDAs. Such approaches are the most related to those we propose here.

The author in [ 11 ] proposes HGLDA , relying on HyperGeometric distribution for LDAs inference, that integrates MDAs and LMIs information. HGLDA has been successfully applied to predict Breast Cancer, Lung Cancer and Colorectal Cancer-related lncRNAs. NcPred [ 10 ] is a resource propagation technique, using a tripartite network where the edges associate each lncRNA with a disease through its targets. The algorithm proposed in [ 10 ] is based on a multilevel resource transfer technique, which computes the weights between each lncRNA-disease pair and, at each step, considers the resource transferred from the previous step. The approach in [ 24 ], referred to as LDA-TG for short in the following, is the antecedent of the approaches proposed here. It relies on the construction of a tripartite graph, built upon MDAs and LMIs. A score is assigned to each possible LDA ( l ,  d ) by considering both their respective interactions with common miRNAs, and the interactions with miRNAs shared by the considered disease d and other lncRNAs in the neighborhood of l on the tripartite graph. The approaches proposed here differ from LDA-TG for two main reasons. First, the score of LDA-TG is different from the one we introduce here, that allows to reach a better accuracy. Second, a further step based on collaborative filtering is considered here, which also improves the accuracy performance. A method for LDAs prediction relying on a matrix completion technique inspired by recommender systems is presented in [ 14 ]. A two-layer multi-weighted nearest-neighbor prediction model is adopted, using a method similar to memory-based collaborative filtering. Weights are assigned to neighbors for reassigning values to the target matrix, that is an adjacency matrix consisting of lncRNAs, diseases and miRNA. SSMF-BLNP [ 25 ] is based on the combination of selective similarity matrix fusion (SSMF) and bidirectional linear neighborhood label propagation (BLNP). In SSMF, self-similarity networks of lncRNAs and diseases are obtained by selective preprocessing and nonlinear iterative fusion. In BLNP, the initial LDAs are employed in both lncRNA and disease directions as label information for linear neighborhood label propagation.

A third category includes approaches based on integrative frameworks, proposed to take into account different types of information related to lncRNAs, such as their interactions with other molecules, their involvement in disorders and diseases, their similarities. This may improve the prediction step, taking into account simultaneously independent factors.

IntNetLncSim [ 26 ] relies on the construction of an integrated network that comprises lncRNA regulatory data, miRNA-mRNA and mRNA-mRNA interactions. The method computes a similarity score for all pairs of lncRNAs in the integrated network, then analyzes the information flow based on random walk with damping. This allows to infer novel LDAs by exploring the function of lncRNAs. SIMCLDA [ 12 ] identifies LDAs by using inductive matrix completion, based on the integration of known LDAs, disease-gene interactions and gene-gene interactions. The main idea in [ 12 ] is to extract feature vectors of lncRNAs and diseases by principal component analysis, and to calculate the interaction profile for a new lncRNA by the interaction profiles. MFLDA [ 27 ] is a Matrix Factorization based LDAs prediction model that first encodes directly (or indirectly) relevant data sources related to lncRNAs or diseases in individual relational data matrices, and presets weights for these matrices. Then, it simultaneously optimizes the weights and low-rank matrix tri-factorization of each relational data matrix. RWSF-BLP , proposed in [ 28 ], applies a random walk-based multi-similarity fusion method to integrate different similarity matrices, mainly based on semantic and expression data, and bidirectional label propagation. The framework LRWRHLDA is proposed in [ 15 ] based on the construction of a global multi-layer network for LDAs prediction. First, four isomorphic networks including a lncRNA similarity network, a disease similarity network, a gene similarity network and a miRNA similarity network are constructed. Then, six heterogeneous networks involving known lncRNA-disease, lncRNA-gene, lncRNA-miRNA, disease-gene, disease-miRNA, and gene-miRNA associations are built to design the multi-layer network. In [ 29 ] the LDAP-WMPS LDA prediction model is proposed, based on weight matrix and projection score. LDAP-WMPS consists on three steps: the first one computes the disease projection score; the second step calculates the lncRNA projection score; the third step fuses the disease projection score and the lncRNA projection score proportionally, then it normalizes them to get the prediction score matrix.

For most of the approaches summarized above, the performance is evaluated using the LOOCV framework, such that each known LDA is left out in turn as a test sample, and how well this test sample is ranked relative to the candidate samples (all the LDAs without the evidence to confirm their relationships) is computed.

The main goal of the research presented here is to provide more accurate computational methods for the prediction of novel LDAs, candidate for experimental validation in laboratory. To this aim, external information on both molecular interactions (e.g., lncRNA-miRNA interactions) and genotype-phenotype associations (e.g., miRNA-disease associations) is assumed to be available. Indeed, while only a restricted number of validated LDAs is yet available, large amounts of interactions between lncRNAs and other molecules (e.g., miRNAs, genes, proteins), as well as associations between these other molecules and diseases, are known and annotated in curated databases.

A commonly recognized assumption is that lncRNAs with similar behaviour in terms of their molecular interactions with other molecules, may also reflect such a similarity for their involvement in the occurrence and progress of disorders and diseases [ 12 ]. This is even more effective if the correlation with diseases is “mediated” by the molecules they interact with. Based on this observation, we have designed three novel prediction methods that all consider the notion of lncRNA “neighbors”, intended as lncRNAs which share common mediators among the molecules they physically interact with. Here, we focus on miRNAs as mediator molecules. However, the proposed approaches are general enough to allow also the inclusion of other different molecules. Relationships among lncRNAs, mediators and diseases are modeled through tripartite graphs in all the proposed approaches (see Fig.  1 that illustrates the flowchart of the presented research pipeline).

Problem statement Let \({\mathcal {L}}=\{l_1, l_2, \ldots , l_h\}\) be a set of lncRNAs and \({\mathcal {D}}=\{d_1, d_2, \ldots , d_k\}\) be a set of diseases. The goal is to return an ordered set of triplets \({\mathcal {R}}=\{\langle l_x, d_y, s_{xy}\rangle \}\) (with \(x\in [1,h]\) , and \(y\in [1,k]\) ), ranked according to the score \(s_{xy}\) .

The top triplets in \({\mathcal {R}}\) correspond to those pairs \((l_x, d_y)\) with most chances to represent putative LDAs which may be considered for further analysis in laboratory, while the triplets in the bottom correspond to lncRNAs and diseases which are unlikely to be related each other. A key aspect for the solution of the problem defined above is the score computation, that is the main aim of the approaches introduced in the following.

NGH: neighborhood based approach

A model of tripartite graph is adopted here to take into account that lncRNAs interacting with common mediators may be involved in common diseases.

Let \(T_{LMD}=\langle I, A \rangle\) be a tripartite graph defined on the three sets of disjoint vertexes L , M and D , such that \((l,m) \in I\) are edges between vertexes \(l \in L\) and \(m \in M\) , \((m,d) \in A\) are edges between vertexes \(m \in M\) and \(d \in D\) , respectively. In particular, L is associated to a set of lncRNAs, M to a set of miRNA and D to a set of diseases. Moreover, edges of the type ( l ,  m ) represent molecular interactions between lncRNAs and miRNA, experimentally validated in laboratory; edges of the type ( m ,  d ) correspond to known miRNA-disease associations, according to the existing literature. In both cases, interactions and associations annotated and stored in public databases may be taken into account.

The following definitions hold.

Definition 1

(Neighbors) Two lncRNAs \(l_h, l_k \in L\) are neighbors in \(T_{LMD}=\langle I, A \rangle\) if there exists at least a \(m_x \in M\) such that \((l_h, m_x) \in I\) and \((l_k, m_x) \in I\) .

Definition 2

(Prediction Score) The Prediction Score for the pair \((l_i,d_j)\) such that \(l_i \in L\) and \(d_j \in D\) is defined as:

\(M_{l_i}\) is the set of annotated miRNA interacting with \(l_i\) ,

\(M_{d_j}\) is the set of miRNA found to be associated to \(d_j\) ,

\(M_{l_x}\) is the set of miRNA interacting with the neighbor \(l_x\) of \(l_i\) (for each neighbor of \(l_i\) ),

\(\alpha\) is a real value in [0, 1] used to balance the two terms of the formula.

Definition 3

(Normalized prediction score) The Normalized Prediction Score for the pair \((l_i,d_j)\) such that \(l_i \in L\) , \(d_j \in D\) and \(s_{ij}\) is the Prediction Score for \((l_i,d_j)\) , is defined as:

NGH-CF: NGH extended with collaborative filtering

We remark that the main idea here is trying to infer the behaviour of a lncRNA, from that of its neighbors. Moreover, it is worth to point out that the notion of neighbor is related to the presence of miRNAs interacting with the same lncRNAs. However, not all the miRNA-lncRNA interactions have already been discovered, and miRNA-disease associations as well. This intuitively reminds to a typical context of data incompleteness where Collaborative Filtering may be successful in supporting the prediction process [ 30 ].

In more detail, what to be encoded by the Collaborative Filter is that lncRNAs presenting similar behaviours in terms of interactions with miRNAs, should reflect such a similarity also in their involvement with the occurrence and progress of diseases, mediated by those miRNAs. To this aim, a matrix R is considered here such that each element \(r_{ij}\) represents if (or to what extent) the lncRNA i and the disease j may be considered related. We call R relationship matrix (it is also known as rating matrix in other contexts, such as for example in the prediction of user-item associations). How to obtain \(r_{ij}\) is at the basis of the two variants of the approach presented in this section.

Due to the fact that R is usually a very sparse matrix, it can be factored into other two matrices L and D such that R \(\approx\) \(L\) \(^T\) \(D\) . In particular, matrix factorization models map both lncRNAs and diseases to a joint latent factor space F of dimensionality f , such that each lncRNA i is associated with a vector \(l_i \in F\) , each disease j with a vector \(d_j \in F\) , and their relationships are modeled as inner products in that space. Indeed, for each lncRNA i , the elements of \(l_i\) measure the extent to which it possesses those latent factors, and the same holds for each disease j and the corresponding elements of \(d_j\) . The resulting dot product in the factor space captures the affinity between lncRNA i and disease j , with reference to the considered latent factors. To this aim, there are two important tasks to be solved:

Mapping lncRNAs and diseases into the corresponding latent factors vectors.

Fill the matrix R , that is, the training set.

To learn the factor vectors \(l_i\) and \(d_j\) , a possible choice is to minimize the regularized squared error on the set of known relationships:

where \(\chi\) is the set of ( i ,  j ) pairs for which \(r_{ij}\) is not equal to zero in the matrix R . To this aim, we apply the ALS technique [ 31 ], which rotates between fixing the \(l_i\) ’s and fixing the \(d_j\) ’s. When all \(l_i\) ’s are fixed, the system recomputes the \(d_j\) ’s by solving a least-squares problem, and vice versa.

Filling the matrix R is performed according to two different criteria, resulting in the two different variants of the approach presented in this section, namely, CF and NGH-CF, respectively. According to the first criteria (CF), \(r_{ij}\) is set equal to 1 if the lncRNA i and the disease j share at least one miRNA in common, to 0 otherwise. The second variant (NGH-CF) works instead as a booster to improve the accuracy of NGH. In this latter case, the matrix R is filled by the normalized score ( 2 ). For both variants, the considered score to rank the predicted LDAs is given by the final value returned by the ALS technique applied on the corresponding matrix R .

Validation methodologies

We remark that the proposed approaches for LDAs prediction return a rank of LDAs, sorted according to the score that is characteristic of the considered approach, such that top triplets may be assumed as the most promising putative LDAs for further analysis in laboratory. As in other contexts [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ], the performance of a prediction tool may be evaluated using suitable external criteria . Here, an external criterion relies on the existence of LDAs that are known to be true from the literature or, even better, from public repositories, where associations already verified in laboratory are annotated. A gold standard is constructed, containing only such true LDAs. The putative LDAs returned by the prediction method can thus be compared against those in the gold standard. In order to work properly, this validation methodology requires the gold standard information to be independent on that considered, in its turn, from the method under evaluation during its prediction task. This is satisfied in our case, due to the fact that all three approaches introduced in the previous sections do not exploit any type of knowledge referred to known LDAs during prediction, relying instead on known miRNA-lncRNA interactions and miRNA-disease associations, which come from independent sources.

According to the above mentioned validation methodology, the proposed approaches can be validated with references to the Receiver Operating Characteristics (ROC) analysis [ 34 ]. In particular, each predicted LDA is associated to a label, that is true if that association is contained in the considered gold standard, and false otherwise.

By varying the threshold value, it is possible to compute the true positive rate (TPR) and the false positive rate (FPR), by refferring to the percentage of the true/false predictions whose ranking is higher/below than the considered threshold value. ROC curve can be drawn by plotting TPR versus FPR at different threshold values. The Area Under ROC Curve (ROC-AUC) is further calculated to evaluate the performance of the tested methods. ROC-AUC equal to 1 indicates perfect performance, ROC-AUC equal to 0.5 random performance.

Similarly to the ROC curve, the Precision-Recall (PR) curve can be drawn as well, combining the positive predictive value (PPV, Precision), i.e., the fraction of predicted LDAs which are true in the gold standard, and the TPR (Recall), in a single visualization, at the threshold varying. The higher on y-axis the obtained curve is, the better the prediction method performance. The Area Under PR curve (AUPR) is more sensitive than AUC to the improvements for the positive class prediction [ 35 ], that is important for the case studied here. Indeed, only true LDAs are known, therefore no negative samples are included in the gold standard.

Another important measure useful to evaluate the prediction accuracy of a method and that can be considered here is the F1-score, defined as the harmonic mean of Precision and Recall to symmetrically represent both metrics in a single one.

We have validated the proposed approaches on both syntetic and real datasets, as explained below.

Synthetic data

A synthetic dataset has been built with 15 lncRNAs, 35 miRNA and 10 diseases, such that three different sets of LDAs may be identified, as follows (see also Table 1 , where the characteristics of each LDA are summarized).

Set 1: 26 LDAs, such that each lncRNA has from 3 to 4 miRNAs shared with the same disease (strongly linked lncRNAs) .

Set 2: 16 LDAs, each lncRNA having only one miRNA shared with a disease, and from 2 to 5 neighbors that are strongly linked with that same disease (directly linked lncRNAs and strong neighborhood) .

Set 3: 12 LDAs involving lncRNAs without any miRNA in common with a certain disease, and a number between 2 and 5 neighbors that are strongly linked with that same disease (only strong neighborhood) .

Experimentally verified data downloaded from starBase [ 36 ] and from HMDD [ 37 ] have been considered for the lncRNA-miRNA interactions and for the miRNA-disease associations, respectively. In particular, the latest version of HMDD, updated at 2019, has been used. Overall, \(1,\!114\) lncRNAs, \(1,\!058\) miRNAs, 885 diseases, \(10,\!112\) lncRNA-miRNA interactions and \(16,\!904\) miRNA-disease associations have been included in the analysis.

In order to evaluate the prediction accuracy of the approaches proposed here against those from the literature, three different gold standards have been considered. A first gold standard dataset GS1 has been obtained from the LncRNA-Disease database [ 38 ], resulting in 183 known and verified LDAs. A second, more restrictive, gold standard GS2 with 157 LDAs has been built by the intersection of data from [ 38 ] and [ 39 ]. Finally, also a larger gold standard dataset GS3 has been included in the analysis, by extracting LDAs from MNDRv2.0 database [ 40 ], where associations both experimentally verified and retrieved from manual literature curation are stored, resulting in 408 known LDAs.

Comparison on real data

The approaches proposed here have been compared against other approaches from the literature, over the three different gold standards described in the previous Section. In particular, all approaches considered from the literature have been run according to the default setting of their parameters, reported on the corresponding scientific publications and/or on their manual instructions.

Our approaches have been compared at first on GS1 against those approaches taking exactly the same input than ours, that are HGLDA [ 11 ], ncPred [ 10 ] and LDA-TG [ 24 ]. In particular, we have implemented HGLDA and used the corresponding p-value score, corrected by FDR as suggested by [ 11 ], for the ROC analysis. Moreover, we have normalized also the scores returned by ncPred and LDA-TG for the predicted LDAs, according to the formula in Definition 3 . Indeed, we have observed experimentally that such a normalization improves the accuracy of both methods from the literature, resulting in a better AUC. As for the novel approaches proposed here, the Normalized Prediction Score has been considered for NGH, while the approximated rating score resulting from ALS [ 31 ] is used for both CF and NGH-CF. Figure  2 shows the AUC scored by each method on GS1, while in Fig.  3 the different ROC curves are plotted. In particular, NGH scores a value of AUC equal to 0.914, thus outperforming the other three methods previously presented in the literature, i.e., HGLDA, ncPred and LDA-TG, that reach 0.876, 0.886 and 0.866, respectively (we remark also that performance of both ncPred and LDA-TG has been slightly improved with respect to their original one, by normalizing their scores). As for the novel approaches based on collaborative filtering, they both present a better accuracy than the others, with CF having AUC equal to 0.957 and NGH-CF to 0.966, respectively. Therefore, these results confirm that taking into account the collaborative effects of lncRNAs and miRNAs is useful to improve LDAs prediction, and the most successful approach is NGH-CF, that is, the neighborhood based approach boosted by collaborative filtering.

Comparison of the scored AUC on GS1

ROC curves for the compared methods on GS1

Another interesting issue is represented by the “agreement” between the different methods taking the same input, in terms of the returned best scoring LDAs. Table 2 shows the Jaccard Index computed between the proposed approaches and those receiving the same input, on the top \(5\%\) LDAs in the corresponding ranks, sorted from the best to the worst score values for each method. It emerges that results by HGLDA and ncPred have a small match with the other approaches (at most 0.23), while NGH-CF has high agreement with CF (0.74), as well as with NGH and LDA-TG (both 0.70). LDA-TG and CF present a sufficient match in their best predictions (0.59). This latter comparison based on agreement shows that approaches based on neighborhood analysis share a larger set of LDAs, in the top part of their ranks.

The proposed approaches have been compared also against other two recent methods from the literature, i.e., SIMCLDA and HGNNLDA, which receive in input different data than ours, including mRNA and drugs. For this reason, the more restrictive gold standard GS2 has been exploited for the comparison, where only lncRNAs and diseases having some correspondences with the additional input data of SIMCLDA and HGNNLDA are included. Figure  4 shows the comparison of the scored AUC on GS2, while Fig.  5 the corresponding ROC curves. In particular, the behaviour of all approaches previously tested does not change significantly on this other gold standard, moreover all the other approaches overcome SIMCLDA. On the other hand, HGNNLDA has a better performance than HGLDA, NcPred and LDA-TG, although it has a worse accuracy than NGH, CF and NGH-CF. The former confirms its superiority with regards to all considered approaches.

Comparison of the scored AUC on GS2

ROC curves for the compared methods on GS2

The proposed approaches have been compared also against LDAP-WMPS on GS3. Figure  6 shows the AUC values scored by all compared approaches on GS3, while Fig.  7 the corresponding ROC curves. In particular, the behaviour of all approaches previously tested does not change on this other gold standard, and LDAP-WMPS has better performance than the other approaches except for NGH, CF, NGH-CF and HGNNLDA.

Comparison of the scored AUC on GS3

ROC curves for the compared methods on GS3

The AUPR values scored by the compared methods on GS1, GS2, and GS3 are shown in Fig.  8 , while the corresponding PR-curves are plotted in Fig.  9 . In particular, for GS1 results are analogous to the ROC analysis, with NGH-CF the best performing one, followed by CF and NGH, while HGLDA is the worst. On GS2, NGH-CF and CF keep their superiority, followed by SMCLDA and NGH, while HGLDA is yet the worst one. On GS3, NGH-CF is the first, Cf the second and both HGNNLDA and LDAP-WMPS outperform NGH, while HGLDA in this case slightly outperforms LDA-TG, ncPred and SMCLDA, which results to be the worst one.

AUPR hystogram for the compared methods on GS1, GS2, GS3

Precision-recall curves for the compared methods on GS1,GS2,GS3

Figures 10 , 11 and 12 show the F1-score values obtained, for all methods compared on GS1, GS2 and GS3, respectively, at the varying of a threshold fixed on the method score. In Tables 3 , 4 and 5 it is shown, for each gold standard, the highest value of F1-score obtained by each considered method, as well as the corresponding Precision and Recall values, and the minimum threshold value for which the highest F1-score value has been reached. On GS1 and GS2, the three best performing approaches are NGH-CF, CF and NGH, in this order. On GS3 the order is the same, and LDAP-WMPS performs equally to NGH.

F1-score for the compared methods on GS1

F1-Score for the compared methods on GS2

F1-Score for the compared methods on GS3

Robustness analysis

The main aim of the analysis discussed here is to measure to what extent the proposed methods are able to correctly recognize verified LDAs, even if part of the existing associations are missed, i.e., the sets of known and verified lncRNA-miRNA interactions and miRNA-disease associations are not complete. This is important to verify that the proposed approaches can provide reliable predictions also in presence of data incompleteness, that is often the case when lncRNAs are involved. Therefore, the robustness of each proposed method has been evaluated by performing progressive alterations of the input associations coming from the real datasets, according to the following three different criteria.

Progressively eliminate the \(5\%\) , \(10\%\) , \(15\%\) and \(20\%\) of lncRNA-miRNA interactions from the input data.

Progressively eliminate the \(5\%\) , \(10\%\) , \(15\%\) and \(20\%\) of miRNA-disease associations from the input data.

Progressively eliminate the \(5\%\) , \(10\%\) , \(15\%\) and \(20\%\) of both lncRNA-miRNA interactions and miRNA-disease associations (half and half), from the input data.

Tests summarized above have been performed for 20 times each. Tables 6 , 7 and 8 show the mean of the AUC values for NGH, CF and NGH-CF, respectively, over the 20 tests. In particular, all methods perform well on the three test typologies at \(5\%\) , the worst being NGH-CF, which however presents an average AUC equal to 0.84 for case 1), that is still a high value. NGH-CF is also the method that presents the best robustness on case 3), keeping the value of 0.92 also at \(20\%\) , while CF is the worst performing in case 3), indeed its average AUC decreases from 0.95 at \(5\%\) to 0.63 already at \(10\%\) , and then to 0.50 at \(20\%\) . This behaviour in case 3), where both lncRNA-miRNA interactions and miRNA-disease associations are progressively eliminated, deserves some observations. Indeed, results show that the combination of neighborhood analysis and collaborative filtering is the most robust one with regards to this perturbation, while collaborative filtering alone is the worst performing. On the other hand, CF results to be the most robust in case 1), where only lncRNA-miRNA interactions are eliminated, and this is due to the fact that CF does not take into account how many miRNAs are shared by pairs of lncRNAs. As for case 2), performance of all methods is comparable and generally good, possibly in consideration of the fact that a large number of miRNA-disease associations are available, therefore discarding small percentages of them does not affect largely the final prediction.

Comparison on specific situations

In this section further experimental tests are described, showing how well the considered methods perform in detecting specific situations, depicted through the synthetic dataset first, and then searched for in the real data. In particular, the basic observation here is that prediction approaches from the literature usually fail in detecting true LDAs, when the involved lncRNAs and diseases do not have a large number of shared miRNAs (referring to those approaches taking the same input than ours). The novel approaches we propose are particularly effective in managing the situation depicted above, through neighborhood analysis and collaborative filtering, allowing to detect similar behaviours shared by different lncRNAs, depending on the miRNAs they interact with.

For each set of LDAs defined in the synthetic data (i.e., set 1, set 2, and set 3), and for each tested method (i.e., HGLDA, NCPRED, NHG, CF, NGH-CF), Table 9 shows the percentage of LDAs in that set which is recognized at the top \(10\%\) , \(20\%\) , \(30\%\) , \(50\%\) of the rank of all LDAs, sorted by the score returned by the considered method. As an example, for HGLDA the \(32\%\) of LDAs of set 1 are located in the top \(10\%\) of its rank, where instead none LDAs in set 2 or 3 find place.

Looking at these results some interesting considerations come out. First of all, for the methods HGLDA, NCPRED, NHG and CF most associations of the set 1 are located in the top \(50\%\) of their corresponding ranks, while NGH-CF has a different behaviour. Indeed, it locates a lower number of such LDAs in the highest part of its rank than the other approaches, possibly due to the fact that it leaves room for a larger number of associations in the other two sets in the top ranked positions. As for LDAs in the set 2, all methods recognize some of them already in the top \(10\%\) , except for HGLDA, as alredy highlighted. The approaches able to recognize the larger percentages of these associations at the top \(50\%\) of their rank are NGH and NGH-CF. LDAs in the set 3 are the most difficult to recognize, due to the fact that the lncRNA and the disease do not share any miRNA in common. Indeed, the worst performing methods in this case are HGLDA, which is able to locate some of these associations only at the top \(50\%\) (according to the percentages we considered here), and NCPRED, which performs slightly better although it reaches the same percentage of located associations than HGLDA at \(50\%\) (the \(28\%\) ). As expected, approaches based on neighborhood analysis and collaborative filtering perform better, with the best one resulting to be NGH-CF.

In the previous section we have shown that all methods proposed here are able to detect specific situations, characterized by the fact that a lncRNA may have very few (or none) common miRNAs with a disease, and its neighbors share instead a large set of miRNAs with that disease. We have checked if this case occurs among the verified LDAs that our approaches find and their competitors do not. Table 10 shows, only by meaning of example, 10 experimentally verified LDAs, included in GS1, that are top ranked for the novel approaches proposed here, whereas they are in the bottom rank of the other approaches from the literature compared on GS1. Six out of such LDAs do not present any common miRNAs between the lncRNA and the disease, while four share only one miRNA. All involved lncRNAs present neighbors with a large number of miRNAs in common with the disease in that LDA, in accordance with the hypothesis that the ability in capturing this situation allows to obtain a better accuracy.

Survival analysis has been also performed by one of the TCGA Computational Tools, that is, TANRIC [ 41 ], on four of the pairs in Table 10 . In particular, those lncRNAs and diseases available in TANRIC have been chosen. Results are reported in Figures 13 , 14 , 15 and 16 , showing that the over-expression of the considered lncRNA determines a lower survival probability over the time, for all four considered cases.

Survival analysis related to SNHG16 and bladder neoplasm

Survival analysis related to CBR3-AS1 and prostate neoplasm

Survival analysis related to MALAT1 and bladder neoplasm

Survival analysis related to MEG3 and breast neoplasm

In the previous sections the effectiveness and robustness of the proposed approaches have been illustrated, showing that all three are able to return reliable predictions, as well as to detect specific situations which may occur in true predictions and are missed by competitors. Here we provide a discussion on some novel LDAs predicted by NGH, CF and NGH-CF.

Table 11 shows seven LDAs which are not present in the considered gold standards, and that have been returned by all three methods proposed here, with highest score. The first of these associations is between CDKN2B-AS1 and LEUKEMIA, confirmed by recent literature [ 42 , 43 ]. Indeed, CDKN2B-AS1 was found to be highly expressed in pediatric T-ALL peripheral blood mononuclear cells [ 42 ], moreover genome-wide association studies show that it is associated to Chronic Lymphocytic Leukaemia risk in Europeans [ 43 ]. As for the second association between DLEU2 and LEUKEMIA, DLEU2 is a long non-coding transcript with several splice variants, which has been identified by [ 44 ] through a comprehensive sequencing of a commonly deleted region in leukemia (i.e., the 13q14 region). Different investigations reported up regulation of this lncRNA in several types of cancers. The lncRNA H19 regulates GLIOMA angiogenesis [ 45 , 46 ], while MAP3K14 is one of the well-recognized biomarkers in the prognosis of renal cancer, which is reminiscent of the pancreatic metastasis from renal cell carcinoma [ 47 ]. MEG3 has been recently found to be important for the prediction of LEUKEMIA risk [ 48 ]. Multiple studies have shown that MIR155HG is highly expressed in diffuse large B-cell (DLBC) lymphoma and primary mediastinal B-cell lymphoma, and in chronic lymphocytic leukemia. The transcription factor MYB activates MIR155HG activity, which causes the epigenetic state of MIR155HG to be dysregulated and causes an abnormal increase in MIR155 [ 49 ]. Also the last top-ranked association in Table 11 between TUG1 and NON-SMALL CELL LUNG CARCINOMA has found evidence in the literature [ 50 , 51 , 52 ].

Tables 12 , 13 , and 14 show the top 100 (sorted by the scores returned by each method) novel LDA predictions that NGH and CF, NGH and NGH-CF, CF and NGH-CF have in common, respectively. Many of the lncRNAs involved in such top-ranked LDAs are not yet characterized in the literature, therefore results presented here may be considered a first attempt to provide novel knowledge about them, through their inferred association with known diseases.

We have explored the application of neighborhood analysis, combined with collaborative filtering, for the improvement of LDAs prediction accuracy. The three approaches proposed here have been evaluated and compared first against their direct competitors from the literature, i.e., the other methods which also use lncRNA-miRNA interactions and miRNA-disease associations, without exploiting a priori known LDAs. It results that all methods proposed here are able to outperform direct competitors, the best one (NGH-CF) also significantly (AUC equal to 0.966 against the 0.886 by NCPRED). In particular, it has been shown that the improvement in accuracy is due to the fact that our approaches capture specific situations neglected by competitors, relying on similar lncRNAs behaviour in terms of their interactions with the considered intermediate molecules (i.e., miRNAs). The proposed approaches have been then compared also against other recent methods, taking different inputs (e.g., integrative approaches), and the experimental evaluation shows that they are able to outperform them as well.

It is worth pointing out the importance of providing reliable data in input to the LDAs prediction approaches. As discussed in this manuscript, information on the lncRNAs relationships with other molecules, and between intermediate molecules and diseases, is provided in input to the proposed approaches. Reliable datasets have been used to perform the experimental analysis provided here. However, as the user may provide also different input datasets, it is important to point out that the reliability of the obtained predictions strictly depends on that of input information.

As neighborhood analysis has resulted to be effective in characterizing lncRNAs with regards to their association with known diseases, we plan to apply it also for predicting possible common functions among lncRNAs, for example by clustering them according to their interactions, which has shown to be successful for other types of molecules [ 53 ]. Moreover, due to the success of integrative approaches on the analysis of biological data [ 54 ], we expect that including other types of intermediate molecules, such as for example genes and proteins, in the main pipeline of the proposed approaches may further improve their accuracy.

In conclusion, the use of reliable input data and the integration of different types of information coming from molecular interactions seem to be the most promising future directions for LDAs prediction.

Availability of data and materials

The source code is available at: https://github.com/marybonomo/LDAsPredictionApproaches.git In particular, executable software for NGH, CF, and NGH-CF are provided, as well as syntetic and real input datasets used here; the three different gold standard datasets GS1, GS2, GS3; the final obtained results.

Medico-Salsench E, et al. The non-coding genome in genetic brain disorders: New targets for therapy? Essays Biochem. 2021;65(4):671–83.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Statello L, Guo CJ, Chen LL, et al. Gene regulation by long non-coding RNAs and its biological functions. Nat Rev Mol Cell Biol. 2021;22:96–118.

Article   CAS   PubMed   Google Scholar  

Zhao H, Shi J, Zhang Y, et al. LncTarD: a manually-curated database of experimentally-supported functional lncRNA–target regulations in human diseases. Nucl Acids Res. 2019;48(D1):D118–D126. ISSN: 0305-1048.

Liao Q, et al. Large-scale prediction of long non-coding RNA functions in a coding-non-coding gene co- expression network. Nuc Acids Res. 2011;39:3864–78.

Article   CAS   Google Scholar  

Chen X, et al. Long non-coding RNAs and complex diseases: from experimental results to computational models. Brief Bioinf. 2017;18(4):558–76.

CAS   Google Scholar  

Wang B, et al. lncRNA-disease association prediction based on matrix decomposition of elastic network and collaborative filtering. Sci Rep. 2022;12:7.

Google Scholar  

He J, et al. HOPEXGB: a consensual model for predicting miRNA/lncRNA-disease associations using a heterogeneous disease-miRNA-lncRNA information network. J Chem Inf Model 2023

Zhong H, et al. Association filtering and generative adversarial networks for predicting lncRNA-associated disease. BMC Bioinf. 2023;24(1):234.

Dengju Y, et al. GCNFORMER: graph convolutional network and transformer for predicting lncRNA-disease associations. BMC Bioinf. 2024;25(1):5.

Article   Google Scholar  

Alaimo S, Giugno R, Pulvirenti A. ncPred: ncRNA-disease association prediction through Tripartite network-based inference. Front Bioeng Biot. 2014;2:71.

Chen X. Predicting lncRNA-disease associations and constructing lncRNA functional similarity network based on the information of miRNA. Sci Rep. 2015;5:13186.

Lu C, et al. Prediction of lncRNA-disease associations based on inductive matrix completion. Bioinformatics. 2018;34(19):3357–64.

Xuan Z, Li J, Yu X, Feng J, et al. A probabilistic matrix factorization method for identifying lncRNA-disease associations. Genes 2019;10(2)

Du X, et al. lncRNA-disease association prediction method based on the nearest neighbor matrix completion model. Sci Rep. 2022;12(1):21653.

Wang L, et al. Prediction of lncRNA-disease association based on a Laplace normalized random walk with restart algorithm on heterogeneous networks. BMC Bioinf. 2022;23(1):1–20.

Huang L, Zhang L, Chen X. Updated review of advances in microRNAs and complex diseases: taxonomy, trends and challenges of computational models. Brief Bioinf. 2022;23(5):bbac358.

Huang L, Zhang L, Chen X. Updated review of advances in microRNAs and complex diseases: experimental results, databases, webservers and data fusion. Brief Bioinf. 2022;23(6):bbac397.

Huang L, Zhang L, Chen X. Updated review of advances in microRNAs and complex diseases: towards systematic evaluation of computational models. Brief Bioinf. 2022;23(6):bbac407.

Chen X, Yan G. Novel human lncRNA-disease association inference based on lncRNA expression profiles. Bioinformatics. 2013;29(20):2617–24.

Xie G, et al. SKF-LDA: similarity kernel fusion for predicting lncRNA-disease association. Mol Therapy-Nucleic Acids. 2019;18:45–55.

Liu D, et al. HGNNLDA: predicting lncRNA-drug sensitivity associations via a dual channel hypergraph neural network. IEEE/ACM transactions on computational biology and bioinformatics, 2023;1–11.

Zhang Y, et al. LDAI-ISPS: lncRNA-disease associations inference based on integrated space projection scores. Int J Molecular Sci. 2020;21(4):1508.

Liang Y, et al. MAGCNSE: predicting lncRNA-disease associations using multi-view attention graph convolutional network and stacking ensemble model. BMC Bioinf. 2022;23(1):189.

Bonomo M, La Placa A, Rombo SE. Prediction of lncRNA-disease associations from tripartite graphs. In: Heterogeneous data management, polystores, and analytics for healthcare - VLDB workshops, poly 2020 and DMAH 2020, virtual event, August 31 and September 4, 2020, Revised Selected Papers. Springer, Berlin, 2020;205–210. ISSN: 978-3-030-71054-5

Xie G, et al. Predicting lncRNA-disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation. Brief Bioinform. 2023;24(1):bbac595.

Article   PubMed   Google Scholar  

Cheng L, et al. ntNetLncSim: an integrative network analysis method to infer human lncRNA functional similarity. Oncotarget. 2016;7(30):47864–74.

Article   PubMed   PubMed Central   Google Scholar  

Guangyuan F, et al. Matrix factorization-based data fusion for the prediction of lncRNA-disease associations. Bioinformatics. 2018;34:1529–37.

Xie G, et al. RWSF-BLP: a novel lncRNA-disease association prediction model using random walk-based multi-similarity fusion and bidirectional label propagation. Mol Genet Genom. 2021;296:473–83.

Wang B, et al. lncRNA-disease association prediction based on the weight matrix and projection score. PLOS One. 2023;18(1): e0278817.

Duan R, Jiang C, Jain HK. Combining review-based collaborative filtering and matrix factorization: a solution to rating’s sparsity problem”. Decis Support Syst 2022;156:113748. ISSN: 0167–9236.

Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems. Computer. 2009;42(8):30–7.

Parida L, Pizzi C, Rombo SE. Irredundant tandem motifs. Theoret Comput Sci. 2014;525:89–102.

Bonomo M, et al. Topological ranks reveal functional knowledge encoded in biological networks: a comparative analysis. Brief Bioinform. 2022;23(3):bbac101.

Fawcett T. An introduction to ROC analysis. Pattern Recognit Lett. 2006;27(8):861–74.

Saito T, Rehmsmeier M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLOS One. 2015;10(3): e0118432.

Li J, et al. starBase v2. 0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res. 2013;42:D92–7.

Li Y, et al. HMDD v2.0: a database for experimentally supported human microRNA and disease associations. Nucleic Acids Res. 2014;42:D1070–4.

Chen G, et al. LncRNADisease: a database for long-non-coding RNA-associated diseases. Nucleic Acids Res. 2013;41:D983–6.

Gao Y, et al. Lnc2Cancer 3.0: an updated resource for experimentally supported lncRNA/circRNA cancer associations and web tools based on RNA-seq and scRNA-seq data. Nucleic Acids Res. 2021;49(D1):D1251–8.

Cui T, et al. MNDR v2. 0: an updated resource of ncRNA-disease associations in mammals. Nucleic Acids Res. 2018;46(D1):D371–4.

CAS   PubMed   Google Scholar  

Li J, et al. TANRIC: an interactive open platform to explore the function of lncRNAs in cancer. Cancer Res. 2015;75(18):3728–37.

Chen L, et al. lncRNA CDKN2B-AS1 contributes to tumorigenesis and chemoresistance in pediatric T-cell acute lymphoblastic leukemia through miR-335-3p/TRAF5 axis. In: Anti-cancer drugs, Wolters Kluwer Health, Inc. (2020)

Song C, et al. CDKN2B-AS1: an indispensable long non-coding RNA in multiple diseases. Current Pharm Des. 2020;26(41):5335–46.

Ghafouri-Fard S, et al. Deleted in lymphocytic leukemia 2 (DLEU2): an lncRNA with dissimilar roles in different cancers. Biomed Pharmacother. 2021;133: 111093.

Jia P, et al. Long non-coding RNA H19 regulates glioma angiogenesis and the biological behavior of glioma-associated endothelial cells by inhibiting microRNA-29a. Cancer Lett. 2016;381(2):359–69.

Liu Z, et al. LncRNA H19 promotes glioma angiogenesis through miR-138/HIF-1 α /VEGFaxis. Neoplasma. 2020;67(1):111–8.

Zhou S, et al. A novel immune-related gene prognostic Index (IRGPI) in pancreatic adenocarcinoma (PAAD) and its implications in the tumor microenvironment. Cancers. 2022;14(22):5652.

Pei J, et al. Novel contribution of long non-coding RNA MEG3 genotype to prediction of childhood leukemia risk. Cancer Genom Proteom. 2022;19(1):27–34.

Peng L, et al. MIR155HG is a prognostic biomarker and associated with immune infiltration and immune checkpoint molecules expression in multiple cancers. Cancer Med. 2019;8(17):7161–73.

Zhang E, et al. P53-regulated long non-coding RNA TUG1 affects cell proliferation in human non-small cell lung cancer, partly through epigenetically regulating HOXB7 expression. Cell Death Dis. 2014;5(5):e1243–e1243.

Lin P, et al. Long noncoding RNA TUG1 is downregulated in non-small cell lung cancer and can regulate CELF1 on binding to PRC2. BMC Cancer. 2016;16:1–10.

Niu Y, et al. Long non-coding RNA TUG1 is involved in cell growth and chemoresistance of small cell lung cancer by regulating LIMK2b via EZH2. Mol Cancer. 2017;16(1):1–13.

Pizzuti C, Rombo SE. An evolutionary restricted neighborhood search clustering approach for PPI networks. Neurocomputing. 2014;145:53–61.

Rombo SE, Ursino D (2021) Integrative bioinformatics and omics data source interoperability in the next-generation sequencing era

Download references

Acknowledgements

The authors are grateful to the Anonymous Reviewers, for the constructive and useful suggestions that allowed to significantly improve the quality of this manuscript. Some of the results shown here are in part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga .

PRIN “multicriteria data structures and algorithms: from compressed to learned indexes, and beyond”, Grant No. 2017WR7SHH, funded by MIUR (closed). “Modelling and analysis of big knowledge graphs for web and medical problem solving” (CUP: E55F22000270001), “Computational Approaches for Decision Support in Precision Medicine” (CUP:E53C22001930001), and “Knowledge graphs e altre rappresentazioni compatte della conoscenza per l’analisi di big data” (CUP: E53C23001670001), funded by INdAM GNCS 2022, 2023, 2024 projects, respectively. “Models and Algorithms relying on knowledge Graphs for sustainable Development goals monitoring and Accomplishment - MAGDA” (CUP: B77G24000050001), funded by the European Union under the PNRR program related to “Future Artificial Intelligence - FAIR”.

Author information

Authors and affiliations.

Kazaam Lab s.r.l., Palermo, Italy

Mariella Bonomo & Simona E. Rombo

Department of Mathematics and Computer Science, University of Palermo, Palermo, Italy

Simona E. Rombo

You can also search for this author in PubMed   Google Scholar

Contributions

MB and SER equally contributed to the research presented in this manuscript. MB implemented and run the software, SER performed the analysis of results. Both authors wrote and reviewed the entire manuscript.

Corresponding author

Correspondence to Mariella Bonomo .

Ethics declarations

Ethics approval and consent to participate.

Not Applicable

Consent for publication

Competing interests.

SER is editor of BMC Bionformatics. MB has no Conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Bonomo, M., Rombo, S.E. Neighborhood based computational approaches for the prediction of lncRNA-disease associations. BMC Bioinformatics 25 , 187 (2024). https://doi.org/10.1186/s12859-024-05777-8

Download citation

Received : 13 December 2023

Accepted : 11 April 2024

Published : 13 May 2024

DOI : https://doi.org/10.1186/s12859-024-05777-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• LncRNA-disease associations
• Molecular interactions
• Bioinformatics
• Long non-coding RNA

BMC Bioinformatics

ISSN: 1471-2105

• General enquiries: [email protected]