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Identifying Compound Risk Factors of Disease by Evolutionary Learning of SNP Combinatorial Features  

Rhee, Je-Keun (서울대학교 생물정보학 협동과정)
Ha, Jung-Woo (서울대학교 컴퓨터공학부)
Bae, Seol-Hui (질병관리본부 국립보건연구원 유전체센터 바이오과학정보과)
Kim, Soo-Jin (서울대학교 컴퓨터공학부 협동과정)
Lee, Min-Su (서울대학교 컴퓨터공학부)
Park, Keun-Joon (질병관리본부 국립보건연구원 유전체센터 바이오과학정보과)
Zhang, Byoung-Tak (서울대학교 컴퓨터공학부)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.15, no.12, 2009 , pp. 928-932 More about this Journal
Abstract
Most diseases are caused by complex processes of various factors. Although previous researches have tried to identify the causes of the disease, there are still lots of limitations to clarify the complex factors. Here, we present a disease classification model based on an evolutionary learning approach of combinatorial features using the data sets from the genetics and cohort studies. We implemented a system for finding the combinatorial risk factors and visualizing the results. Our results show that the proposed method not only improves classification accuracy but also identifies biologically meaningful sets of risk factors.
Keywords
Machine Learning; Evolutionary Learning; Bioinformatics; Feature Selection;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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