Prediction of Heterodimeric Protein Complexes from Weighted Protein-Protein Interaction Networks Using Novel Features and Kernel Functions

• Item type: SIG Technical Reports(1)
• 公開日: 2013-12-04
• タイトル: Prediction of Heterodimeric Protein Complexes from Weighted Protein-Protein Interaction Networks Using Novel Features and Kernel Functions
• タイトル / 言語: en
• タイトル: Prediction of Heterodimeric Protein Complexes from Weighted Protein-Protein Interaction Networks Using Novel Features and Kernel Functions
• 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: Institute of Mathematics for Industry, Kyushu 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): Institute of Mathematics for Industry, Kyushu University
• 著者所属(英): en
• 著者所属(英): Bioinformatics Center, Institute for Chemical Research, Kyoto University
• 著者名: Peiying, Ruan; Morihiro, Hayashida; Osamu, Maruyama; Tatsuya, Akutsu
• 著者名(英): Peiying, Ruan; Morihiro, Hayashida; Osamu, Maruyama; Tatsuya, Akutsu
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Identification of protein complexes is very useful because many proteins express their functional activity by interacting with other proteins and forming protein complexes. For that purpose, many prediction methods for protein complexes from protein-protein interactions have been developed such as MCL, MCODE, RNSC, PCP, RRW, and NWE. These methods have dealt with only complexes with size of more than three because the methods often are based on some density of subgraphs. However, heterodimeric protein complexes that consist of two distinct proteins occupy a large part according to several comprehensive databases of known complexes. In this technical report, we propose several feature space mappings from protein-protein interaction data, in which each interaction is weighted based on reliability. Furthermore, we make use of prior knowledge on protein domains to develop feature space mappings, domain composition kernel and its combination kernel with our proposed features. We perform ten-fold cross-validation computational experiments. These results suggest that our proposed kernel considerably outperforms the naive Bayes-based method, which is the best existing method for predicting heterodimeric protein complexes.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Identification of protein complexes is very useful because many proteins express their functional activity by interacting with other proteins and forming protein complexes. For that purpose, many prediction methods for protein complexes from protein-protein interactions have been developed such as MCL, MCODE, RNSC, PCP, RRW, and NWE. These methods have dealt with only complexes with size of more than three because the methods often are based on some density of subgraphs. However, heterodimeric protein complexes that consist of two distinct proteins occupy a large part according to several comprehensive databases of known complexes. In this technical report, we propose several feature space mappings from protein-protein interaction data, in which each interaction is weighted based on reliability. Furthermore, we make use of prior knowledge on protein domains to develop feature space mappings, domain composition kernel and its combination kernel with our proposed features. We perform ten-fold cross-validation computational experiments. These results suggest that our proposed kernel considerably outperforms the naive Bayes-based method, which is the best existing method for predicting heterodimeric protein complexes.
• 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-96, 号 1, p. 1-6, 発行日 2013-12-04
• 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