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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: © 2017 |Pages: 22
ISBN13: 9781522517597|ISBN10: 1522517596|EISBN13: 9781522517603
DOI: 10.4018/978-1-5225-1759-7.ch019
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MLA

Binder, Hans, et al. "Personalized Disease Phenotypes from Massive OMICs Data." Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2017, pp. 441-462. https://doi.org/10.4018/978-1-5225-1759-7.ch019

APA

Binder, H., Hopp, L., Lembcke, K., & Wirth, H. (2017). Personalized Disease Phenotypes from Massive OMICs Data. In I. Management Association (Ed.), Artificial Intelligence: Concepts, Methodologies, Tools, and Applications (pp. 441-462). IGI Global. https://doi.org/10.4018/978-1-5225-1759-7.ch019

Chicago

Binder, Hans, et al. "Personalized Disease Phenotypes from Massive OMICs Data." In Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 441-462. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-1759-7.ch019

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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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