Identification of functional modules in protein networks by near-clique detection

• Item type: SIG Technical Reports(1)
• 公開日: 2013-03-14
• タイトル: Identification of functional modules in protein networks by near-clique detection
• タイトル / 言語: en
• タイトル: Identification of functional modules in protein networks by near-clique detection
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: 遺伝子制御・ネットワーク
• 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: Graduate School of Information Sciences, Tohoku University
• 著者所属: Graduate School of Information Sciences, Tohoku University
• 著者所属: Graduate School of Information Sciences, Tohoku University／Tohoku Medical Megabank Organization, Tohoku University／Institute of Development, Aging, and Cancer, Tohoku University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Sciences, Tohoku University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Sciences, Tohoku University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Sciences, Tohoku University / Tohoku Medical Megabank Organization, Tohoku University / Institute of Development, Aging, and Cancer, Tohoku University
• 著者名: Shu, Tadaka; Takeshi, Obayashi; Kengo, Kinoshita
• 著者名(英): Shu, Tadaka; Takeshi, Obayashi; Kengo, Kinoshita
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: In analysis of protein-protein interaction (PPI) networks, detection of functional module is one of the most important problems for understanding of cellular function of uncharacterized proteins. Identification of functional modules has been mainly done by searching densely connected subgraph, and some methods have been proposed to identify modules by using different criteria of densely connected subgraph. Here, we propose a new method NCMine to detect functional modules by extracting near-clique subgraphs aiming to get better identification of functional modules. We tested NCMine and other methods by using human PPI network from HPRD. When NCMine is applied to the network, it extracts about 2000 modules and 55% of them have at least one enriched GO term that is shared among members of a module. On the other hand, the percentage of GO-enriched modules extracted by other methods was lower than that of NCMine. This indicates that NCMine is superior to other methods in identification of biologically meaningful modules.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: In analysis of protein-protein interaction (PPI) networks, detection of functional module is one of the most important problems for understanding of cellular function of uncharacterized proteins. Identification of functional modules has been mainly done by searching densely connected subgraph, and some methods have been proposed to identify modules by using different criteria of densely connected subgraph. Here, we propose a new method NCMine to detect functional modules by extracting near-clique subgraphs aiming to get better identification of functional modules. We tested NCMine and other methods by using human PPI network from HPRD. When NCMine is applied to the network, it extracts about 2000 modules and 55% of them have at least one enriched GO term that is shared among members of a module. On the other hand, the percentage of GO-enriched modules extracted by other methods was lower than that of NCMine. This indicates that NCMine is superior to other methods in identification of biologically meaningful modules.
• 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: AA12055912
• 書誌情報: 研究報告バイオ情報学（BIO）; 巻 2013-BIO-33, 号 4, p. 1-2, 発行日 2013-03-14
• 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