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Personalized Disease Phenotypes from Massive OMICs Data

Personalized Disease Phenotypes from Massive OMICs Data

Hans Binder, Lydia Hopp, Kathrin Lembcke, Henry Wirth
Copyright: © 2015 |Pages: 20
ISBN13: 9781466666115|ISBN10: 1466666110|EISBN13: 9781466666122
DOI: 10.4018/978-1-4666-6611-5.ch015
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MLA

Binder, Hans, et al. "Personalized Disease Phenotypes from Massive OMICs Data." Big Data Analytics in Bioinformatics and Healthcare, edited by Baoying Wang, et al., IGI Global, 2015, pp. 359-378. https://doi.org/10.4018/978-1-4666-6611-5.ch015

APA

Binder, H., Hopp, L., Lembcke, K., & Wirth, H. (2015). Personalized Disease Phenotypes from Massive OMICs Data. In B. Wang, R. Li, & W. Perrizo (Eds.), Big Data Analytics in Bioinformatics and Healthcare (pp. 359-378). IGI Global. https://doi.org/10.4018/978-1-4666-6611-5.ch015

Chicago

Binder, Hans, et al. "Personalized Disease Phenotypes from Massive OMICs Data." In Big Data Analytics in Bioinformatics and Healthcare, edited by Baoying Wang, Ruowang Li, and William Perrizo, 359-378. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-6611-5.ch015

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Abstract

Application of new high-throughput technologies in molecular medicine collects massive data for hundreds to thousands of persons in large cohort studies by characterizing the phenotype of each individual on a personalized basis. The chapter aims at increasing our understanding of disease genesis and progression and to improve diagnosis and treatment. New methods are needed to handle such “big data.” Machine learning enables one to recognize and to visualize complex data patterns and to make decisions potentially relevant for diagnosis and treatment. The authors address these tasks by applying the method of self-organizing maps and present worked examples from different disease entities of the colon ranging from inflammation to cancer.

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