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Mutation probability of cytochrome P450 based on a genetic algorithm and support vector machine
Author(s) -
Yao Yu,
Zhang Tao,
Xiong Yi,
Li Li,
Huo Juan,
Wei DongQing
Publication year - 2011
Publication title -
biotechnology journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.144
H-Index - 84
eISSN - 1860-7314
pISSN - 1860-6768
DOI - 10.1002/biot.201000450
Subject(s) - support vector machine , mutation , computer science , feature selection , artificial intelligence , genetic algorithm , machine learning , selection (genetic algorithm) , pattern recognition (psychology) , genetics , biology , gene
The support vector machine (SVM), an effective statistical learning method, has been widely used in mutation prediction. Two factors, i.e., feature selection and parameter setting, have shown great influence on the efficiency and accuracy of SVM classification. In this study, according to the principles of a genetic algorithm (GA) and SVM, we developed a GA‐SVM program and applied it to human cytochrome P450s (CYP450s), which are important monooxygenases in phase I drug metabolism. The program optimizes features and parameters simultaneously, and hence fewer features are used and the overall prediction accuracy is improved. We focus on the mutation of non‐synonymous single nucleotide polymorphisms (nsSNPs) in protein sequences that appear to exhibit significant influences on drug metabolism. The final predictive model has a quite satisfactory performance, with the prediction accuracy of 61% and cross‐validation accuracy of 73%. The results indicate that the GA‐SVM program is a powerful tool in optimizing mutation predictive models of nsSNPs of human CYP450s.