Improved feature-based prediction of SNPs in human cytochrome P450 enzymes

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• 作者：Li Li (1)
  Yi Xiong (1)
  Zhuo-Yu Zhang (1)
  Quan Guo (1)
  Qin Xu (1)
  Hien-Haw Liow (2) (3)
  Yong-Hong Zhang (1) (4)
  Dong-Qing Wei (1)
  
  1. State Key Laboratory of Microbial Metabolism and College of Life Science and Biotechnology ; Shanghai Jiao Tong University ; Shanghai ; 200240 ; China
  2. Center for Genome Sciences and Systems Biology ; Washington University in St. Louis ; St. Louis ; MO ; 63130 ; USA
  3. Department of Mathematics ; Washington University in St. Louis ; St. Louis ; MO ; 63130 ; USA
  4. Medicine Engineering Research Center and School of Pharmacy ; Chongqing Medical University ; Chongqing ; 400016 ; China
  
• 关键词：single nucleotide polymorphisms ; support vector machine ; prediction
• 刊名：Interdisciplinary Sciences: Computational Life Sciences
• 出版年：2015
• 出版时间：March 2015
• 年：2015
• 卷：7
• 期：1
• 页码：65-77
• 全文大小：642 KB
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• 刊物主题：Computer Appl. in Life Sciences; Computational Biology/Bioinformatics; Statistics for Life Sciences, Medicine, Health Sciences; Theoretical and Computational Chemistry; Theoretical, Mathematical and Computational Physics; Computational Science and Engineering;
• 出版者：International Association of Scientists in the Interdisciplinary Areas
• ISSN：1867-1462

文摘

Single nucleotide polymorphisms (SNPs) make up the most common form of mutations in human cytochrome P450 enzymes family, and have the potential to bring with different drug responses or specific diseases in individual patients. Here, based on machine learning technology, we aim to explore an effective set of sequence-based features for improving prediction of SNPs by using support vector machine algorithms. The features are derived from the target residues and flanking protein sequences, such as amino acid types, sequences composition, physicochemical properties, position-specific scoring matrix, phylogenetic entropy and the number of possible codons of target residues. In order to deal with the imbalance data with a majority of non-SNPs and a minority of SNPs, a preprocessing strategy based on fuzzy set theory was applied to the datasets. Our final model achieves the performance of 93.8% in sensitivity, 88.8% in specificity, 91.3% in accuracy and 0.971 of AUC value, which is significantly higher than the previous DNA sequence-based or protein sequence-based methods. Furthermore, our study also suggested the roles of individual features for prediction of SNPs. The most important features consist of the amino acid type, the number of available codons, position-specific scoring matrix and phylogenetic entropy. The improved model will be a promising tool for SNP predictions, and assist in the research of genome mutation and personalized prescriptions.