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The Block Hidden Markov Model for Biological Sequence Analysis

The Block Hidden Markov Model for Biological Sequence Analysis
The Block Hidden Markov Model for Biological Sequence Analysis
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.
0302-9743
64-70
Won, Kyoung-Jae
3b5c7d9a-e6bd-4624-9825-338e795b9945
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e
Krogh, Anders
4a5cfccf-5403-45fb-96c5-61f60bef7d79
Mircea Gh, Mircea Gh
c683236d-3bb4-4c6e-9f7a-1d221578f87a
Howlett, Robert J.
7535b674-d9c3-4015-85ff-24477c698926
Jain, Lakhmi C.
6118444e-870a-4f4e-8248-279ae870404d
Won, Kyoung-Jae
3b5c7d9a-e6bd-4624-9825-338e795b9945
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e
Krogh, Anders
4a5cfccf-5403-45fb-96c5-61f60bef7d79
Mircea Gh, Mircea Gh
c683236d-3bb4-4c6e-9f7a-1d221578f87a
Howlett, Robert J.
7535b674-d9c3-4015-85ff-24477c698926
Jain, Lakhmi C.
6118444e-870a-4f4e-8248-279ae870404d

Won, Kyoung-Jae, Prugel-Bennett, Adam and Krogh, Anders , Mircea Gh, Mircea Gh, Howlett, Robert J. and Jain, Lakhmi C. (eds.) (2004) The Block Hidden Markov Model for Biological Sequence Analysis. Lecture Notes in Computer Science, 3213, 64-70.

Record type: Article

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.

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More information

Published date: October 2004
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 260704
URI: https://eprints.soton.ac.uk/id/eprint/260704
ISSN: 0302-9743
PURE UUID: ebb3d878-ad2e-4016-a195-6c17cdd1491e

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Date deposited: 30 Mar 2005
Last modified: 16 Jul 2019 22:50

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Contributors

Author: Kyoung-Jae Won
Author: Adam Prugel-Bennett
Author: Anders Krogh
Editor: Mircea Gh Mircea Gh
Editor: Robert J. Howlett
Editor: Lakhmi C. Jain

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