Inference of disease E3s from integrated functional relation network

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dc.contributor.authorMin, Bumki-
dc.contributor.authorYi, Gwan Su-
dc.date.accessioned2017-01-16T01:54:02Z-
dc.date.available2017-01-16T01:54:02Z-
dc.date.created2017-01-03-
dc.date.issued2014-11-07-
dc.identifier.citation8th ACM International Workshop on Data and Text Mining in Biomedical Informatics, DTMBIO 2014, pp.13 - 16-
dc.identifier.urihttp://hdl.handle.net/10203/219586-
dc.description.abstractRecently, the potential of E3 ligase as a therapeutic target is increasing. The systematic method to derive disease-related E3s can provide significant contribution for this demand. Several disease gene prediction methods have been introduced but it is hard to find E3 ligase-specific information from them. We have developed a unique approach to prioritize the disease relation of E3 by integrating E3-substrate relations and their neighboring network with known disease genes. The potential of our method is demonstrated by showing better performance against the previous methods to predict known disease relations of E3. We could discover 101 E3s and their functional network having 1,285 relations with diseases. Our method will provide new promising chances in drug target discovery field as well as disease mechanism study.-
dc.languageEnglish-
dc.publisherAssociation for Computing Machinery, Inc-
dc.titleInference of disease E3s from integrated functional relation network-
dc.typeConference-
dc.type.rimsCONF-
dc.citation.beginningpage13-
dc.citation.endingpage16-
dc.citation.publicationname8th ACM International Workshop on Data and Text Mining in Biomedical Informatics, DTMBIO 2014-
dc.identifier.conferencecountryCC-
dc.identifier.conferencelocationShanghai, China-
dc.identifier.doi10.1145/2665970.2665979-
dc.contributor.localauthorYi, Gwan Su-
dc.contributor.nonIdAuthorMin, Bumki-
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BiS-Conference Papers(학술회의논문)
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