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The Block Hidden Markov Model for Biological Sequence Analysis. / Won, Kyoung Jae; Prügel-Bennett, Adam; Krogh, Anders.
Knowledge-BasedIntelligent Informationand Engineering Systems: 8th International Conference, KES 2004, Proceedings. ed. / Mircea Gh. Negoita; Robert J. Howlett; Lakhmi Jain. Vol. 1 Springer, 2004. p. 64-70 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 3213).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Won, KJ, Prügel-Bennett, A
& Krogh, A 2004,
The Block Hidden Markov Model for Biological Sequence Analysis. in MG Negoita, RJ Howlett & L Jain (eds),
Knowledge-BasedIntelligent Informationand Engineering Systems: 8th International Conference, KES 2004, Proceedings. vol. 1, Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3213, pp. 64-70, 8th International conference, KES 2004, Wellington, New Zealand,
20/09/2004.
https://doi.org/10.1007/978-3-540-30132-5_13APA
Won, K. J., Prügel-Bennett, A.
, & Krogh, A. (2004).
The Block Hidden Markov Model for Biological Sequence Analysis. In M. G. Negoita, R. J. Howlett, & L. Jain (Eds.),
Knowledge-BasedIntelligent Informationand Engineering Systems: 8th International Conference, KES 2004, Proceedings (Vol. 1, pp. 64-70). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 3213
https://doi.org/10.1007/978-3-540-30132-5_13Vancouver
Won KJ, Prügel-Bennett A
, Krogh A.
The Block Hidden Markov Model for Biological Sequence Analysis. In Negoita MG, Howlett RJ, Jain L, editors, Knowledge-BasedIntelligent Informationand Engineering Systems: 8th International Conference, KES 2004, Proceedings. Vol. 1. Springer. 2004. p. 64-70. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 3213).
https://doi.org/10.1007/978-3-540-30132-5_13Author
Won, Kyoung Jae ; Prügel-Bennett, Adam ; Krogh, Anders. / The Block Hidden Markov Model for Biological Sequence Analysis. Knowledge-BasedIntelligent Informationand Engineering Systems: 8th International Conference, KES 2004, Proceedings. editor / Mircea Gh. Negoita ; Robert J. Howlett ; Lakhmi Jain. Vol. 1 Springer, 2004. pp. 64-70 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 3213).
Bibtex
@inproceedings{cd75a457149b426cbcb4207da3b0385a,
title = "The Block Hidden Markov Model for Biological Sequence Analysis",
abstract = "The Hidden Markov Models (HMMs) are widely used for biological sequence analysis because of their ability to incorporate biological information in their structure. An automatic means of optimising the structure of HMMs would be highly desirable. To maintain biologically interpretable blocks inside the HMM, we used a Genetic Algorithm (GA) that has HMM blocks in its coding representation. We developed special genetics operations that maintain the useful HMM blocks. To prevent over-fitting a separate data set is used for comparing the performance of the HMMs to that used for the Baum-Welch training. The performance of this algorithm is applied to finding HMM structures for the promoter and coding region of C. jejuni. The GA-HMM was capable of finding a superior HMM to a hand-coded HMM designed for the same task which has been published in the literature.",
author = "Won, {Kyoung Jae} and Adam Pr{\"u}gel-Bennett and Anders Krogh",
year = "2004",
doi = "10.1007/978-3-540-30132-5_13",
language = "English",
isbn = "3-540-23318-0",
volume = "1",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "64--70",
editor = "Negoita, {Mircea Gh.} and Howlett, {Robert J.} and Lakhmi Jain",
booktitle = "Knowledge-BasedIntelligent Informationand Engineering Systems",
address = "Switzerland",
note = "8th International conference, KES 2004 ; Conference date: 20-09-2004 Through 25-09-2004",
}
RIS
TY - GEN
T1 - The Block Hidden Markov Model for Biological Sequence Analysis
AU - Won, Kyoung Jae
AU - Prügel-Bennett, Adam
AU - Krogh, Anders
PY - 2004
Y1 - 2004
N2 - The Hidden Markov Models (HMMs) are widely used for biological sequence analysis because of their ability to incorporate biological information in their structure. An automatic means of optimising the structure of HMMs would be highly desirable. To maintain biologically interpretable blocks inside the HMM, we used a Genetic Algorithm (GA) that has HMM blocks in its coding representation. We developed special genetics operations that maintain the useful HMM blocks. To prevent over-fitting a separate data set is used for comparing the performance of the HMMs to that used for the Baum-Welch training. The performance of this algorithm is applied to finding HMM structures for the promoter and coding region of C. jejuni. The GA-HMM was capable of finding a superior HMM to a hand-coded HMM designed for the same task which has been published in the literature.
AB - The Hidden Markov Models (HMMs) are widely used for biological sequence analysis because of their ability to incorporate biological information in their structure. An automatic means of optimising the structure of HMMs would be highly desirable. To maintain biologically interpretable blocks inside the HMM, we used a Genetic Algorithm (GA) that has HMM blocks in its coding representation. We developed special genetics operations that maintain the useful HMM blocks. To prevent over-fitting a separate data set is used for comparing the performance of the HMMs to that used for the Baum-Welch training. The performance of this algorithm is applied to finding HMM structures for the promoter and coding region of C. jejuni. The GA-HMM was capable of finding a superior HMM to a hand-coded HMM designed for the same task which has been published in the literature.
U2 - 10.1007/978-3-540-30132-5_13
DO - 10.1007/978-3-540-30132-5_13
M3 - Article in proceedings
AN - SCOPUS:31744440287
SN - 3-540-23318-0
SN - 978-3-540-23318-3
VL - 1
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 64
EP - 70
BT - Knowledge-BasedIntelligent Informationand Engineering Systems
A2 - Negoita, Mircea Gh.
A2 - Howlett, Robert J.
A2 - Jain, Lakhmi
PB - Springer
T2 - 8th International conference, KES 2004
Y2 - 20 September 2004 through 25 September 2004
ER -
