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Personalized Disease Phenotypes from Massive OMICs Data
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Author(s): Hans Binder (Interdisciplinary Center for Bioinformatics, University of Leipzig, Germany), Lydia Hopp (University of Leipzig, Germany), Kathrin Lembcke (University of Leipzig, Germany)and Henry Wirth (Interdisciplinary Center for Bioinformatics, University of Leipzig, Germany)
Copyright: 2017
Pages: 22
Source title:
Artificial Intelligence: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 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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