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Integrative Literature and Data Mining to Rank Disease Candidate Genes

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Biomedical Literature Mining

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1159))

Abstract

While the genomics-derived discoveries promise benefits to basic research and health care, the speed and affordability of sequencing following recent technological advances has further aggravated the data deluge. Seamless integration of the ever-increasing clinical, genomic, and experimental data and efficient mining for knowledge extraction, delivering actionable insight and generating testable hypotheses are therefore critical for the needs of biomedical research. For instance, high-throughput techniques are frequently applied to detect disease candidate genes. Experimental validation of these candidates however is both time-consuming and expensive. Hence, several computational approaches based on literature and data mining have been developed to identify the most promising candidates for follow-up studies. Based on “guilt by association” principle, most of these methods use prior knowledge about a disease of interest to discover and rank novel candidate genes. In this chapter, we provide a brief overview of recent advances made in literature- and data-mining-based approaches for candidate gene prioritization. As a case study, we focus on a Web-based computational approach that uses integrated heterogeneous data sources including gene–literature associations for ranking disease candidate genes and explain how to run typical queries using this system.

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Authors and Affiliations

  1. Department of Computer Science, College of Engineering and Applied Science, University of Cincinnati, Cincinnati, OH, USA

    Chao Wu, Cheng Zhu & Anil G. Jegga D.V.M., M.S.

  2. Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Avenue, MLC 7024, Cincinnati, OH, 45229, USA

    Chao Wu, Cheng Zhu & Anil G. Jegga D.V.M., M.S.

  3. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, 45229, USA

    Anil G. Jegga D.V.M., M.S.

  4. Genzyme Corporation, Cambridge, MA, 02142, USA

    Cheng Zhu

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  1. Chao Wu
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  2. Cheng Zhu
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  3. Anil G. Jegga D.V.M., M.S.
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Correspondence to Anil G. Jegga D.V.M., M.S. .

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Editors and Affiliations

  1. GlaxoSmithKline, King of Prussia, Pennsylvania, USA

    Vinod D. Kumar

  2. GlaxoSmithKline, Hitchin, Hertfordshire, United Kingdom

    Hannah Jane Tipney

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Wu, C., Zhu, C., Jegga, A.G. (2014). Integrative Literature and Data Mining to Rank Disease Candidate Genes. In: Kumar, V., Tipney, H. (eds) Biomedical Literature Mining. Methods in Molecular Biology, vol 1159. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-0709-0_12

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  • DOI: https://doi.org/10.1007/978-1-4939-0709-0_12

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