title: Prediction of protein-protein interaction strength using domain features with supervised regression.
abstract: Proteins in living organisms express various important functions by interacting with other proteins and molecules. Therefore, many efforts have been made to investigate and predict protein-protein interactions (PPIs). Analysis of strengths of PPIs is also important because such strengths are involved in functionality of proteins. In this paper, we propose several feature space mappings from protein pairs using protein domain information to predict strengths of PPIs. Moreover, we perform computational experiments employing two machine learning methods, support vector regression (SVR) and relevance vector machine (RVM), for dataset obtained from biological experiments. The prediction results showed that both SVR and RVM with our proposed features outperformed the best existing method.
authors.affiliation: Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan.
authors.forename: Mayumi
authors.identifier: 0000-0002-2555-7345
authors.initials: M
authors.lastname: Kamada
authors.affiliation: Japan Ichiba Section Development Unit, Rakuten Inc., 4-12-3 Higashi-shinagawa, Shinagawa-ku, Tokyo 140-0002, Japan.
authors.forename: Yusuke
authors.initials: Y
authors.lastname: Sakuma
authors.affiliation: Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.
authors.forename: Morihiro
authors.identifier: 0000-0001-7961-0175
authors.initials: M
authors.lastname: Hayashida
authors.affiliation: Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.
authors.forename: Tatsuya
authors.identifier: 0000-0001-9763-797X
authors.initials: T
authors.lastname: Akutsu
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping
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