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Protein complex prediction via bottleneck-based graph partitioning

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dc.contributor.authorAhn, J.-
dc.contributor.authorLee, D.H.-
dc.contributor.authorYoon, Y.-
dc.contributor.authorYeu, Y.-
dc.contributor.authorPark, S.-
dc.date.available2020-02-29T09:46:05Z-
dc.date.created2020-02-11-
dc.date.issued2012-
dc.identifier.issn0000-0000-
dc.identifier.urihttps://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/17522-
dc.description.abstractDetecting protein complexes is one of essential and fundamental tasks in understanding various biological functions or processes. Therefore, precise identification of protein complexes is indispensible. For more precise detection of protein complexes, we propose a novel data structure which employs bottleneck proteins as partitioning points for detecting the protein complexes. The partitioning process allows overlapping between resulting protein complexes. We applied our algorithm to several PPI (Protein-Protein Interaction) networks of Saccharomyces cerevisiae and Homo sapiens, and validated our results using public databases of protein complexes. Our algorithm resulted in overlapping protein complexes with significantly improved F1 score, which comes from higher precision.-
dc.language영어-
dc.language.isoen-
dc.relation.isPartOfInternational Conference on Information and Knowledge Management, Proceedings-
dc.subjectBiological functions-
dc.subjectBottleneck proteins-
dc.subjectGraph Partitioning-
dc.subjectHomo sapiens-
dc.subjectIdentification of proteins-
dc.subjectNetwork Clustering-
dc.subjectProtein complexes-
dc.subjectProtein-protein interactions-
dc.subjectPublic database-
dc.subjectAlgorithms-
dc.subjectBioinformatics-
dc.subjectComplexation-
dc.subjectData mining-
dc.subjectData structures-
dc.subjectKnowledge management-
dc.subjectYeast-
dc.subjectProteins-
dc.titleProtein complex prediction via bottleneck-based graph partitioning-
dc.typeArticle-
dc.type.rimsART-
dc.description.journalClass1-
dc.identifier.doi10.1145/2390068.2390079-
dc.identifier.bibliographicCitationInternational Conference on Information and Knowledge Management, Proceedings, pp.49 - 55-
dc.identifier.scopusid2-s2.0-84870482645-
dc.citation.endPage55-
dc.citation.startPage49-
dc.citation.titleInternational Conference on Information and Knowledge Management, Proceedings-
dc.contributor.affiliatedAuthorYoon, Y.-
dc.type.docTypeConference Paper-
dc.subject.keywordAuthorBottleneck protein-
dc.subject.keywordAuthorNetwork clustering-
dc.subject.keywordAuthorProtein complex detection-
dc.subject.keywordAuthorProtein-protein interaction-
dc.subject.keywordPlusBiological functions-
dc.subject.keywordPlusBottleneck proteins-
dc.subject.keywordPlusGraph Partitioning-
dc.subject.keywordPlusHomo sapiens-
dc.subject.keywordPlusIdentification of proteins-
dc.subject.keywordPlusNetwork Clustering-
dc.subject.keywordPlusProtein complexes-
dc.subject.keywordPlusProtein-protein interactions-
dc.subject.keywordPlusPublic database-
dc.subject.keywordPlusAlgorithms-
dc.subject.keywordPlusBioinformatics-
dc.subject.keywordPlusComplexation-
dc.subject.keywordPlusData mining-
dc.subject.keywordPlusData structures-
dc.subject.keywordPlusKnowledge management-
dc.subject.keywordPlusYeast-
dc.subject.keywordPlusProteins-
dc.description.journalRegisteredClassscopus-
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