title: A new supervised over-sampling algorithm with application to protein-nucleotide binding residue prediction.
abstract: Protein-nucleotide interactions are ubiquitous in a wide variety of biological processes. Accurately identifying interaction residues solely from protein sequences is useful for both protein function annotation and drug design, especially in the post-genomic era, as large volumes of protein data have not been functionally annotated. Protein-nucleotide binding residue prediction is a typical imbalanced learning problem, where binding residues are extremely fewer in number than non-binding residues. Alleviating the severity of class imbalance has been demonstrated to be a promising means of improving the prediction performance of a machine-learning-based predictor for class imbalance problems. However, little attention has been paid to the negative impact of class imbalance on protein-nucleotide binding residue prediction. In this study, we propose a new supervised over-sampling algorithm that synthesizes additional minority class samples to address class imbalance. The experimental results from protein-nucleotide interaction datasets demonstrate that the proposed supervised over-sampling algorithm can relieve the severity of class imbalance and help to improve prediction performance. Based on the proposed over-sampling algorithm, a predictor, called TargetSOS, is implemented for protein-nucleotide binding residue prediction. Cross-validation tests and independent validation tests demonstrate the effectiveness of TargetSOS. The web-server and datasets used in this study are freely available at http://www.csbio.sjtu.edu.cn/bioinf/TargetSOS/.
authors.affiliation: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Hu
authors.affiliation: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China.
authors.forename: Xue
authors.initials: X
authors.lastname: He
authors.affiliation: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China; Changshu Institute, Nanjing University of Science and Technology, Changshu, Jiangsu, China.
authors.forename: Dong-Jun
authors.initials: DJ
authors.lastname: Yu
authors.affiliation: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China; School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China.
authors.forename: Xi-Bei
authors.initials: XB
authors.lastname: Yang
authors.affiliation: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China.
authors.forename: Jing-Yu
authors.initials: JY
authors.lastname: Yang
authors.affiliation: Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Hong-Bin
authors.initials: HB
authors.lastname: Shen
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D009711:Nucleotides / Q000378:metabolism*; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D000069553:Supervised Machine Learning; D060388:Support Vector Machine
chemical_list: D009711:Nucleotides; D011506:Proteins