A Clustering Method for Analysis of Sequence Similarity Networks of Proteins Using Maximal Components of Graphs

• Item type: Trans(1)
• 公開日: 2008-03-15
• タイトル: A Clustering Method for Analysis of Sequence Similarity Networks of Proteins Using Maximal Components of Graphs
• タイトル / 言語: en
• タイトル: A Clustering Method for Analysis of Sequence Similarity Networks of Proteins Using Maximal Components of Graphs
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: Original Papers
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 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: Department of Applied Mathematics and Physics Graduate School of Informatics 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): Department of Applied Mathematics and Physics, Graduate School of Informatics, Kyoto University
• 著者名: Morihiro, Hayashida; Tatsuya, Akutsu; Hiroshi, Nagamochi
• 著者名(英): Morihiro, Hayashida; Tatsuya, Akutsu; Hiroshi, Nagamochi
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: This paper proposes a novel clustering method based on graph theory for analysis of biological networks. In this method each biological network is treated as an undirected graph and edges are weighted based on similarities of nodes. Then maximal components which are defined based on edge connectivity are computed and the nodes are partitioned into clusters by selecting disjoint maximal components. The proposed method was applied to clustering of protein sequences and was compared with conventional clustering methods. The obtained clusters were evaluated using P-values for GO (GeneOntology) terms. The average P-values for the proposed method were better than those for other methods.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: This paper proposes a novel clustering method based on graph theory for analysis of biological networks. In this method, each biological network is treated as an undirected graph and edges are weighted based on similarities of nodes. Then, maximal components, which are defined based on edge connectivity, are computed and the nodes are partitioned into clusters by selecting disjoint maximal components. The proposed method was applied to clustering of protein sequences and was compared with conventional clustering methods. The obtained clusters were evaluated using P-values for GO (GeneOntology) terms. The average P-values for the proposed method were better than those for other methods.
• 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: AA12177013
• 書誌情報: IPSJ Transactions on Bioinformatics （TBIO）; 巻 49, 号 SIG5(TBIO4), p. 15-24, 発行日 2008-03-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-6679
• 出版者 / 言語: ja
• 出版者: 情報処理学会