QSAR modeling of peptide biological activity by coupling support vector machine with particle swarm optimization algorithm and genetic algorithm

Journal of Molecular Graphics & Modelling
Xuan ZhouXiaoyong Zou

Abstract

A novel method coupling particle swarm optimization algorithm (PSO) and genetic algorithm (GA) was proposed to optimize simultaneously the kernel parameters of support vector machine (SVM) and determine the optimized features subset. By coupling GA with PSO, the particles produced in each generation in PSO algorithm were processed by crossover and mutation of GA, and then the particles could keep diversity to escape from local optima and find the global optima quickly and accurately. In order to evaluate the proposed method, four peptide datasets were employed for the investigation of quantitative structure-activity relationship (QSAR). The structural and physicochemical features of peptides from amino acid sequences were used to represent peptides for QSAR. The correlation coefficients (R) of training set of the four datasets were 1.0000, 0.9508, 1.0000, 0.9995, the R of test set of the four datasets were 0.9922, 0.9687, 0.9022, 0.7404, respectively. The root-mean-square errors (RMSEs) of training set of the four datasets were 0.0000, 0.0986, 0.0000, 0.0203, the RMSEs of test set of the four datasets were 0.2522, 0.2782, 0.9625, 0.2928, respectively. A protein dataset, which consists of 277 proteins, was also employed to evalu...Continue Reading

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Citations

May 12, 2012·Journal of Theoretical Biology·Bahram HemmateenejadMaryam Elyasi
Oct 12, 2015·Computational Biology and Chemistry·Mariya A ToropovaAndrey A Toropov
Oct 16, 2016·Molecular Informatics·Jianbo TongKangnan Li
Oct 10, 2013·Journal of Computational Chemistry·Mahesh R BorkarEvans C Coutinho
Jun 23, 2020·Chemical Biology & Drug Design·Faiza Hanif WaghuSusan Idicula-Thomas
Aug 4, 2012·Journal of Chemical Information and Modeling·Yunierkis Pérez-CastilloAnn Nowé

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