title: Prediction of Protein-Protein Interactions by Evidence Combining Methods.
abstract: Most cellular functions involve proteins' features based on their physical interactions with other partner proteins. Sketching a map of protein-protein interactions (PPIs) is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy.
authors.affiliation: National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China. longkaichang@163.com.|College of Informatics, Huazhong Agricultural University, Wuhan 430070, China. longkaichang@163.com.
authors.forename: Ji-Wei
authors.initials: JW
authors.lastname: Chang
authors.affiliation: National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China. zhyq2611@163.com.|College of Informatics, Huazhong Agricultural University, Wuhan 430070, China. zhyq2611@163.com.
authors.forename: Yan-Qing
authors.initials: YQ
authors.lastname: Zhou
authors.affiliation: National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China. m.tahirulqamar@hotmail.com.|College of Informatics, Huazhong Agricultural University, Wuhan 430070, China. m.tahirulqamar@hotmail.com.
authors.forename: Muhammad Tahir
authors.initials: MT
authors.lastname: Ul Qamar
authors.affiliation: National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China. llchen@mail.hzau.edu.cn.|College of Informatics, Huazhong Agricultural University, Wuhan 430070, China. llchen@mail.hzau.edu.cn.
authors.forename: Ling-Ling
authors.initials: LL
authors.lastname: Chen
authors.affiliation: National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China. dingyuduan@mail.hzau.edu.cn.|College of Informatics, Huazhong Agricultural University, Wuhan 430070, China. dingyuduan@mail.hzau.edu.cn.
authors.forename: Yu-Duan
authors.initials: YD
authors.lastname: Ding
mesh_terms: D000818:Animals; D017360:Arabidopsis / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D057225:Data Mining / Q000706:statistics & numerical data*; D066264:Datasets as Topic; D004331:Drosophila melanogaster / Q000378:metabolism; D004926:Escherichia coli / Q000378:metabolism; D063990:Gene Ontology; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D051379:Mice; D008958:Models, Molecular; D058977:Molecular Sequence Annotation; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping / Q000379:methods / Q000706:statistics & numerical data*; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D060388:Support Vector Machine
chemical_list: D011506:Proteins
keywords: PPIs; interaction prediction; physical interactions; support vector machine