Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이상호 | * |
dc.contributor.author | 박현석 | * |
dc.contributor.author | 김민경 | * |
dc.date.accessioned | 2018-06-02T08:14:15Z | - |
dc.date.available | 2018-06-02T08:14:15Z | - |
dc.date.issued | 2005 | * |
dc.identifier.isbn | 3540288961 | * |
dc.identifier.isbn | 9783540288961 | * |
dc.identifier.issn | 0302-9743 | * |
dc.identifier.other | OAK-17666 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/244055 | - |
dc.description.abstract | Transmembrane 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.language | English | * |
dc.title | A hybrid approach to combine HMM and SVM methods for the prediction of the transmembrane spanning region | * |
dc.type | Conference Paper | * |
dc.relation.volume | 3683 LNAI | * |
dc.relation.index | SCOPUS | * |
dc.relation.startpage | 792 | * |
dc.relation.lastpage | 798 | * |
dc.relation.journaltitle | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | * |
dc.identifier.scopusid | 2-s2.0-33745325937 | * |
dc.author.google | Kim M.K. | * |
dc.author.google | Song C.H. | * |
dc.author.google | Yoo S.J. | * |
dc.author.google | Lee S.H. | * |
dc.author.google | Park H.S. | * |
dc.contributor.scopusid | 이상호(56812941400) | * |
dc.contributor.scopusid | 박현석(22433646000) | * |
dc.date.modifydate | 20240325111445 | * |