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Interdisciplinary Sciences: Computational Life Sciences : Volume 6

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Interdisciplinary Sciences: Computational Life Sciences : Volume 4

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Interdisciplinary Sciences: Computational Life Sciences : Volume 2

Interdisciplinary Sciences: Computational Life Sciences : Volume 1

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

Content Provider	Springer Nature Link
Author	Li, Li Xiong, Yi Zhang, Zhuo Yu Guo, Quan Xu, Qin Liow, Hien Haw Zhang, Yong Hong Wei, Dong Qing
Copyright Year	2015
Abstract	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.
Starting Page	65
Ending Page	77
Page Count	13
File Format	PDF
ISSN	19132751
Journal	Interdisciplinary Sciences: Computational Life Sciences
Volume Number	7
Issue Number	1
e-ISSN	18671462
Language	English
Publisher	International Association of Scientists in the Interdisciplinary Areas
Publisher Date	2015-03-21
Publisher Place	Quebec
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	single nucleotide polymorphisms support vector machine prediction Computer Application 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
Content Type	Text
Resource Type	Article
Subject	Biochemistry, Genetics and Molecular Biology Health Informatics Computer Science Applications

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