title: Ens-PPI: A Novel Ensemble Classifier for Predicting the Interactions of Proteins Using Autocovariance Transformation from PSSM.
abstract: Protein-Protein Interactions (PPIs) play vital roles in most biological activities. Although the development of high-throughput biological technologies has generated considerable PPI data for various organisms, many problems are still far from being solved. A number of computational methods based on machine learning have been developed to facilitate the identification of novel PPIs. In this study, a novel predictor was designed using the Rotation Forest (RF) algorithm combined with Autocovariance (AC) features extracted from the Position-Specific Scoring Matrix (PSSM). More specifically, the PSSMs are generated using the information of protein amino acids sequence. Then, an effective sequence-based features representation, Autocovariance, is employed to extract features from PSSMs. Finally, the RF model is used as a classifier to distinguish between the interacting and noninteracting protein pairs. The proposed method achieves promising prediction performance when performed on the PPIs of Yeast, H. pylori, and independent datasets. The good results show that the proposed model is suitable for PPIs prediction and could also provide a useful supplementary tool for solving other bioinformatics problems.
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China.
authors.forename: Zhen-Guo
authors.initials: ZG
authors.lastname: Gao
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China; College of Information Science and Engineering, Zaozhuang University, Zaozhuang, Shandong 277100, China.
authors.forename: Lei
authors.identifier: 0000-0003-0184-307X
authors.initials: L
authors.lastname: Wang
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China.
authors.forename: Shi-Xiong
authors.identifier: 0000-0003-4473-6274
authors.initials: SX
authors.lastname: Xia
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China.
authors.forename: Zhu-Hong
authors.identifier: 0000-0003-1266-2696
authors.initials: ZH
authors.lastname: You
authors.affiliation: School of Foreign Languages, Zaozhuang University, Zaozhuang, Shandong 277100, China.
authors.forename: Xin
authors.identifier: 0000-0002-5053-5447
authors.initials: X
authors.lastname: Yan
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D016480:Helicobacter pylori / Q000235:genetics; D000069550:Machine Learning; D060066:Protein Interaction Maps / Q000235:genetics*; D011506:Proteins / Q000235:genetics*; D012441:Saccharomyces cerevisiae / Q000235:genetics
chemical_list: D011506:Proteins