Boosting Granular Support Vector Machines for the Accurate Prediction of Protein-Nucleotide Binding Sites

Yi-Heng       Zhu; Jun       Hu; Yong       Qi; Xiao-Ning       Song; Dong-Jun       Yu

doi:10.2174/1386207322666190925125524

Abstract

Aim and Objective: The accurate identification of protein-ligand binding sites helps elucidate protein function and facilitate the design of new drugs. Machine-learning-based methods have been widely used for the prediction of protein-ligand binding sites. Nevertheless, the severe class imbalance phenomenon, where the number of nonbinding (majority) residues is far greater than that of binding (minority) residues, has a negative impact on the performance of such machine-learning-based predictors.

Materials and Methods: In this study, we aim to relieve the negative impact of class imbalance by Boosting Multiple Granular Support Vector Machines (BGSVM). In BGSVM, each base SVM is trained on a granular training subset consisting of all minority samples and some reasonably selected majority samples. The efficacy of BGSVM for dealing with class imbalance was validated by benchmarking it with several typical imbalance learning algorithms. We further implemented a protein-nucleotide binding site predictor, called BGSVM-NUC, with the BGSVM algorithm.

Results: Rigorous cross-validation and independent validation tests for five types of proteinnucleotide interactions demonstrated that the proposed BGSVM-NUC achieves promising prediction performance and outperforms several popular sequence-based protein-nucleotide binding site predictors. The BGSVM-NUC web server is freely available at http://csbio.njust.edu.cn/bioinf/BGSVM-NUC/ for academic use.

Keywords: Imbalance learning, granular computing, support vector machine, classifier ensemble, protein-nucleotide binding sites.

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DOI https://dx.doi.org/10.2174/1386207322666190925125524	Print ISSN 1386-2073
Publisher Name Bentham Science Publisher	Online ISSN 1875-5402

Combinatorial Chemistry & High Throughput Screening

Boosting Granular Support Vector Machines for the Accurate Prediction of Protein-Nucleotide Binding Sites

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