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dc.contributor.author이상호*
dc.contributor.author박현석*
dc.contributor.author김민경*
dc.date.accessioned2018-06-02T08:14:15Z-
dc.date.available2018-06-02T08:14:15Z-
dc.date.issued2005*
dc.identifier.isbn3540288961*
dc.identifier.isbn9783540288961*
dc.identifier.issn0302-9743*
dc.identifier.otherOAK-17666*
dc.identifier.urihttps://dspace.ewha.ac.kr/handle/2015.oak/244055-
dc.description.abstractTransmembrane proteins are the primary targets for the development of new drugs, and a number of algorithms that predict transmembrane topology are publicly available on the Web. In this paper, we present a novel approach using both SVM and HMM methods and we demonstrate that our system outperform the previous systems which only use either HMM methods or SVM methods alone. © Springer-Verlag Berlin Heidelberg 2005.*
dc.languageEnglish*
dc.titleA hybrid approach to combine HMM and SVM methods for the prediction of the transmembrane spanning region*
dc.typeConference Paper*
dc.relation.volume3683 LNAI*
dc.relation.indexSCOPUS*
dc.relation.startpage792*
dc.relation.lastpage798*
dc.relation.journaltitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)*
dc.identifier.scopusid2-s2.0-33745325937*
dc.author.googleKim M.K.*
dc.author.googleSong C.H.*
dc.author.googleYoo S.J.*
dc.author.googleLee S.H.*
dc.author.googlePark H.S.*
dc.contributor.scopusid이상호(56812941400)*
dc.contributor.scopusid박현석(22433646000)*
dc.date.modifydate20240325111445*
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인공지능대학 > 컴퓨터공학과 > Journal papers
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