title: Detecting protein-protein interactions with a novel matrix-based protein sequence representation and support vector machines.
abstract: Proteins and their interactions lie at the heart of most underlying biological processes. Consequently, correct detection of protein-protein interactions (PPIs) is of fundamental importance to understand the molecular mechanisms in biological systems. Although the convenience brought by high-throughput experiment in technological advances makes it possible to detect a large amount of PPIs, the data generated through these methods is unreliable and may not be completely inclusive of all possible PPIs. Targeting at this problem, this study develops a novel computational approach to effectively detect the protein interactions. This approach is proposed based on a novel matrix-based representation of protein sequence combined with the algorithm of support vector machine (SVM), which fully considers the sequence order and dipeptide information of the protein primary sequence. When performed on yeast PPIs datasets, the proposed method can reach 90.06% prediction accuracy with 94.37% specificity at the sensitivity of 85.74%, indicating that this predictor is a useful tool to predict PPIs. Achieved results also demonstrate that our approach can be a helpful supplement for the interactions that have been detected experimentally.
authors.affiliation: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China.
authors.forename: Zhu-Hong
authors.initials: ZH
authors.lastname: You
authors.affiliation: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China.
authors.forename: Jianqiang
authors.initials: J
authors.lastname: Li
authors.affiliation: Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Suzhou, Jiangsu 215163, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Gao
authors.affiliation: College of Information Science and Engineering, Guilin University of Technology, Guilin, Guangxi 541004, China.
authors.forename: Zhou
authors.initials: Z
authors.lastname: He
authors.affiliation: School of Electronics and Information Engineering, Tongji University, Shanghai 200092, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Zhu
authors.affiliation: School of Electronics and Information Engineering, Tongji University, Shanghai 200092, China.
authors.forename: Ying-Ke
authors.initials: YK
authors.lastname: Lei
authors.affiliation: School of Electronics and Information Engineering, Tongji University, Shanghai 200092, China.
authors.forename: Zhiwei
authors.identifier: 0000-0001-5781-3465
authors.initials: Z
authors.lastname: Ji
mesh_terms: D000595:Amino Acid Sequence / Q000235:genetics*; D019295:Computational Biology; D030562:Databases, Protein; D016480:Helicobacter pylori / Q000235:genetics; D006801:Humans; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000235:genetics*; D012441:Saccharomyces cerevisiae; D020539:Sequence Analysis, Protein; D060388:Support Vector Machine
chemical_list: D011506:Proteins