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| Yasuyuki Tomita, Hiroyuki Honda, Mitsuhiro Yokota, "Classification method for prediction of multifactorial disease development using interaction between genetic and environmental factors," 2005 IEEE Computational Systems Bioinformatics Conference - Workshops, pp. 247-248, 2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05), 2005. | |||
| BibTex | x | ||
| @article{ 10.1109/CSBW.2005.36, author = {Yasuyuki Tomita and Hiroyuki Honda and Mitsuhiro Yokota}, title = {Classification method for prediction of multifactorial disease development using interaction between genetic and environmental factors}, journal ={2005 IEEE Computational Systems Bioinformatics Conference - Workshops}, volume = {0}, year = {2005}, isbn = {0-7695-2442-7}, pages = {247-248}, doi = {http://doi.ieeecomputersociety.org/10.1109/CSBW.2005.36}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - 2005 IEEE Computational Systems Bioinformatics Conference - Workshops TI - Classification method for prediction of multifactorial disease development using interaction between genetic and environmental factors SN - 0-7695-2442-7 SP247 EP248 A1 - Yasuyuki Tomita, A1 - Hiroyuki Honda, A1 - Mitsuhiro Yokota, PY - 2005 KW - null VL - 0 JA - 2005 IEEE Computational Systems Bioinformatics Conference - Workshops ER - | |||
Multifactorial disease such as life style related diseases, for example, cancer, diabetes mellitus, myocardial infarction (MI) and others, is thought to be caused by complex interactions between polygenic basis and various environmental factors. In this study, we used 22 polymorphisms on 16 candidate genes that have been characterized and potentially associated with MI in terms of biological function and 6 environmental factors. To predict development for MI and classify the subjects into personally optimum development patterns, we extracted risk factor candidates (RFCs) composed of state which is a derivative form of polymorphisms and environmental factors using statistical test and selected risk factors from RFCs using Criterion of Detecting Personal Group (CDPG) defined in this study. We could predict development of blinded data simulated as unknown their development more than 80% accuracy and identify their causal factors using CDPG.