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

Min Kyung Kim; Chul Hwan Song; Seong Joon Yoo; Sang Ho Lee; Hyun Seok Park

doi:10.1007/11553939_112

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

Min Kyung Kim, Chul Hwan Song, Seong Joon Yoo, Sang Ho Lee, Hyun Seok Park

Department of Computer Science & Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings
Publisher	Springer Verlag
Pages	792-798
Number of pages	7
ISBN (Print)	3540288961, 9783540288961
DOIs	https://doi.org/10.1007/11553939_112
State	Published - 2005
Event	9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 - Melbourne, Australia Duration: 14 Sep 2005 → 16 Sep 2005

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3683 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005
Country/Territory	Australia
City	Melbourne
Period	14/09/05 → 16/09/05

Access to Document

10.1007/11553939_112

Cite this

Kim, M. K., Song, C. H., Yoo, S. J., Lee, S. H., & Park, H. S. (2005). A hybrid approach to combine HMM and SVM methods for the prediction of the transmembrane spanning region. In Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings (pp. 792-798). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3683 LNAI). Springer Verlag. https://doi.org/10.1007/11553939_112

Kim, Min Kyung ; Song, Chul Hwan ; Yoo, Seong Joon et al. / A hybrid approach to combine HMM and SVM methods for the prediction of the transmembrane spanning region. Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. Springer Verlag, 2005. pp. 792-798 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f0b2fbeffc6942ecba781c8ee62cb7e5,

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

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.",

author = "Kim, {Min Kyung} and Song, {Chul Hwan} and Yoo, {Seong Joon} and Lee, {Sang Ho} and Park, {Hyun Seok}",

year = "2005",

doi = "10.1007/11553939_112",

language = "English",

isbn = "3540288961",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "792--798",

booktitle = "Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings",

note = "9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 ; Conference date: 14-09-2005 Through 16-09-2005",

}

Kim, MK, Song, CH, Yoo, SJ, Lee, SH & Park, HS 2005, A hybrid approach to combine HMM and SVM methods for the prediction of the transmembrane spanning region. in Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3683 LNAI, Springer Verlag, pp. 792-798, 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005, Melbourne, Australia, 14/09/05. https://doi.org/10.1007/11553939_112

A hybrid approach to combine HMM and SVM methods for the prediction of the transmembrane spanning region. / Kim, Min Kyung; Song, Chul Hwan; Yoo, Seong Joon et al.
Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. Springer Verlag, 2005. p. 792-798 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3683 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Kim MK, Song CH, Yoo SJ, Lee SH, Park HS. A hybrid approach to combine HMM and SVM methods for the prediction of the transmembrane spanning region. In Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. Springer Verlag. 2005. p. 792-798. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11553939_112

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

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