title: Advancing the prediction accuracy of protein-protein interactions by utilizing evolutionary information from position-specific scoring matrix and ensemble classifier.
abstract: Protein-Protein Interactions (PPIs) are essential to most biological processes and play a critical role in most cellular functions. With the development of high-throughput biological techniques and in silico methods, a large number of PPI data have been generated for various organisms, but many problems remain unsolved. These factors promoted the development of the in silico methods based on machine learning to predict PPIs. In this study, we propose a novel method by combining ensemble Rotation Forest (RF) classifier and Discrete Cosine Transform (DCT) algorithm to predict the interactions among proteins. Specifically, the protein amino acids sequence is transformed into Position-Specific Scoring Matrix (PSSM) containing biological evolution information, and then the feature vector is extracted to present protein evolutionary information using DCT algorithm; finally, the ensemble rotation forest model is used to predict whether a given protein pair is interacting or not. When performed on Yeast and H. pylori data sets, the proposed method achieved excellent results with an average accuracy of 98.54% and 88.27%. In addition, we achieved good prediction accuracy of 98.08%, 92.75%, 98.87% and 98.72% on independent data sets (C.elegans, E.coli, H.sapiens and M.musculus). In order to further evaluate the performance of our method, we compare it with the state-of-the-art Support Vector Machine (SVM) classifier and get good results. As a web server, the source code and Yeast data sets used in this article are freely available at http://202.119.201.126:8888/DCTRF/.
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.initials: L
authors.lastname: Wang
authors.affiliation: The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China. Electronic address: zhuhongyou@ms.xjb.ac.cn.
authors.forename: Zhu-Hong
authors.initials: ZH
authors.lastname: You
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China. Electronic address: xiasx@cumt.edu.cn.
authors.forename: Shi-Xiong
authors.initials: SX
authors.lastname: Xia
authors.affiliation: China National Coal Association, Beijing 100713, China.
authors.forename: Feng
authors.initials: F
authors.lastname: Liu
authors.affiliation: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China.
authors.forename: Xing
authors.initials: X
authors.lastname: Chen
authors.affiliation: School of Foreign Languages, Zaozhuang University, Zaozhuang, Shandong 277100, China.
authors.forename: Xin
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; D000818:Animals; D003198:Computer Simulation; D030562:Databases, Protein; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism; D029968:Escherichia coli Proteins / Q000235:genetics / Q000378:metabolism; D019143:Evolution, Molecular; D016480:Helicobacter pylori / Q000235:genetics / Q000378:metabolism; D006801:Humans; D051379:Mice; D008957:Models, Genetic; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism; D060388:Support Vector Machine
chemical_list: D029968:Escherichia coli Proteins; D029701:Saccharomyces cerevisiae Proteins
keywords: Cancer; Multiple sequences alignments; Position-specific scoring matrix; Rotation forest