Inferring Strengths of Protein-Protein Interactions Using Support Vector Regression

• Item type: SIG Technical Reports(1)
• 公開日: 2013-07-15
• タイトル: Inferring Strengths of Protein-Protein Interactions Using Support Vector Regression
• タイトル / 言語: en
• タイトル: Inferring Strengths of Protein-Protein Interactions Using Support Vector Regression
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Bioinformatics Center, Institute for Chemical Research, Kyoto University
• 著者所属: Bioinformatics Center, Institute for Chemical Research, Kyoto University
• 著者所属: Bioinformatics Center, Institute for Chemical Research, Kyoto University
• 著者所属: Bioinformatics Center, Institute for Chemical Research, Kyoto University
• 著者所属(英): en
• 著者所属(英): Bioinformatics Center, Institute for Chemical Research, Kyoto University
• 著者所属(英): en
• 著者所属(英): Bioinformatics Center, Institute for Chemical Research, Kyoto University
• 著者所属(英): en
• 著者所属(英): Bioinformatics Center, Institute for Chemical Research, Kyoto University
• 著者所属(英): en
• 著者所属(英): Bioinformatics Center, Institute for Chemical Research, Kyoto University
• 著者名: Yusuke, Sakuma; Mayumi, Kamada; Morihiro, Hayashida; Tatsuya, Akutsu
• 著者名(英): Yusuke, Sakuma; Mayumi, Kamada; Morihiro, Hayashida; Tatsuya, Akutsu
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Due to the importance of protein-protein interactions (PPIs) in living organisms, many efforts have been made to investigate and predict PPIs. Analysis of strengths of PPIs is important as well as PPIs because such strengths are involved in functionality of proteins. In this technical report, we propose several feature space mappings from protein pairs, which make use of protein domain information, and perform five-fold cross-validation for data obtained from biological experiments. The result of average root mean square error (RMSE) using support vector regression (SVR) with our proposed feature was better than that by the best existing method, APM proposed by Chen et al.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Due to the importance of protein-protein interactions (PPIs) in living organisms, many efforts have been made to investigate and predict PPIs. Analysis of strengths of PPIs is important as well as PPIs because such strengths are involved in functionality of proteins. In this technical report, we propose several feature space mappings from protein pairs, which make use of protein domain information, and perform five-fold cross-validation for data obtained from biological experiments. The result of average root mean square error (RMSE) using support vector regression (SVR) with our proposed feature was better than that by the best existing method, APM proposed by Chen et al.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN10505667
• 書誌情報: 研究報告数理モデル化と問題解決（MPS）; 巻 2013-MPS-94, 号 1, p. 1-4, 発行日 2013-07-15
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会