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Indexed by:期刊论文

Date of Publication:2006-12-22

Journal:FEBS LETTERS

Included Journals:SCIE、Scopus

Volume:580

Issue:30

Page Number:6800-6806

ISSN No.:0014-5793

Key Words:prediction; deleterious nsSNPs; ABCB transporters; computational

Abstract:The non-synonymous SNPs (nsSNPs) in coding regions, neutral or deleterious, could lead to the alteration of the function or structure of proteins. We have developed the computational models to analyze the deleterious nsSNPs in the transporters and predict ones in ABCB (ATP-binding cassette B) transporters of interest. The RPLS (ridge partial least square) and LDA (linear discriminant analysis) methods were applied to the problem, by training on a selection of datasets from a specified source, i.e., human transporters. The best combination of datasets and prediction attributes was ascertained. The prediction accuracy of the theoretical RPLS model for the training and testing sets is 84.8%, and 80.4%, respectively (LDA: 84.3'% and 80.4%), which indicates the models are reasonable and may be helpful for pharmacogenetics studies. (c) 2006 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.