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A hybrid approach to combine HMM and SVM methods for the prediction of the transmembrane spanning region

Title
A hybrid approach to combine HMM and SVM methods for the prediction of the transmembrane spanning region
Authors
Kim M.K.Song C.H.Yoo S.J.Lee S.H.Park H.S.
Ewha Authors
이상호박현석김민경
SCOPUS Author ID
이상호scopus; 박현석scopus
Issue Date
2005
Journal Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN
0302-9743JCR Link
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) vol. 3683 LNAI, pp. 792 - 798
Indexed
SCOPUS scopus
Document Type
Conference Paper
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.
ISBN
3540288961

9783540288961
Appears in Collections:
인공지능대학 > 컴퓨터공학과 > Journal papers
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