title: Predicting protein-protein interactions from sequence using correlation coefficient and high-quality interaction dataset.
abstract: Identifying protein-protein interactions (PPIs) is critical for understanding the cellular function of the proteins and the machinery of a proteome. Data of PPIs derived from high-throughput technologies are often incomplete and noisy. Therefore, it is important to develop computational methods and high-quality interaction dataset for predicting PPIs. A sequence-based method is proposed by combining correlation coefficient (CC) transformation and support vector machine (SVM). CC transformation not only adequately considers the neighboring effect of protein sequence but describes the level of CC between two protein sequences. A gold standard positives (interacting) dataset MIPS Core and a gold standard negatives (non-interacting) dataset GO-NEG of yeast Saccharomyces cerevisiae were mined to objectively evaluate the above method and attenuate the bias. The SVM model combined with CC transformation yielded the best performance with a high accuracy of 87.94% using gold standard positives and gold standard negatives datasets. The source code of MATLAB and the datasets are available on request under smgsmg@mail.ustc.edu.cn.
authors.affiliation: Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, 230031 Hefei, China.
authors.forename: Ming-Guang
authors.initials: MG
authors.lastname: Shi
authors.forename: Jun-Feng
authors.initials: JF
authors.lastname: Xia
authors.forename: Xue-Ling
authors.initials: XL
authors.lastname: Li
authors.forename: De-Shuang
authors.initials: DS
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry*; D001185:Artificial Intelligence; D001426:Bacterial Proteins / Q000737:chemistry / Q000378:metabolism; D019295:Computational Biology / Q000379:methods; D057225:Data Mining; D030562:Databases, Protein; D016480:Helicobacter pylori; D008954:Models, Biological; D011485:Protein Binding; D025941:Protein Interaction Mapping; D020543:Proteome / Q000737:chemistry* / Q000378:metabolism*; D040901:Proteomics / Q000379:methods; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry* / Q000378:metabolism*
chemical_list: D000596:Amino Acids; D001426:Bacterial Proteins; D020543:Proteome; D029701:Saccharomyces cerevisiae Proteins