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Training HMM Structure with Genetic Algorithm for Biological Sequence Analysis

Training HMM Structure with Genetic Algorithm for Biological Sequence Analysis
Training HMM Structure with Genetic Algorithm for Biological Sequence Analysis
Motivation : 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. However, this raises two important issues; firstly, the new HMMs should be biologically interpretable, and secondly we need to control the complexity of the HMM so that it has good generalisation performance on unseen sequences. In this paper we explore the possibility of using a Genetic Algorithm (GA) for optimising the HMM structure. GAs are sufficiently flexible to allow incorporation of other techniques such as Baum-Welch training within their evolutionary cycle. Furthermore, operators which alter the structure of HMMs can be designed to favour interpretable and simple structures. Results : In this paper a training strategy using Genetic Algorithms is proposed, and it is tested on finding HMM structures for the promoter and coding region of the bacterium C. jejuni. The proposed Genetic Algorithm for Hidden Markov Models (GA-HMM) allows HMMs with different numbers of states to evolve. 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 GA-HMM was capable of finding an HMM comparable to a hand-coded HMM designed for the same task, which has previously been published.
1367-4803
3613-3619
Won, K.-J.
d31da8cc-2e91-4167-a6d3-46ea3d772712
Prugel-Bennett, A.
b107a151-1751-4d8b-b8db-2c395ac4e14e
Krogh, A.
d7021dd3-b076-4c19-8ff4-1aaff316e906
Won, K.-J.
d31da8cc-2e91-4167-a6d3-46ea3d772712
Prugel-Bennett, A.
b107a151-1751-4d8b-b8db-2c395ac4e14e
Krogh, A.
d7021dd3-b076-4c19-8ff4-1aaff316e906

Won, K.-J., Prugel-Bennett, A. and Krogh, A. (2004) Training HMM Structure with Genetic Algorithm for Biological Sequence Analysis. Bioinformatics, 20, 3613-3619.

Record type: Article

Abstract

Motivation : 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. However, this raises two important issues; firstly, the new HMMs should be biologically interpretable, and secondly we need to control the complexity of the HMM so that it has good generalisation performance on unseen sequences. In this paper we explore the possibility of using a Genetic Algorithm (GA) for optimising the HMM structure. GAs are sufficiently flexible to allow incorporation of other techniques such as Baum-Welch training within their evolutionary cycle. Furthermore, operators which alter the structure of HMMs can be designed to favour interpretable and simple structures. Results : In this paper a training strategy using Genetic Algorithms is proposed, and it is tested on finding HMM structures for the promoter and coding region of the bacterium C. jejuni. The proposed Genetic Algorithm for Hidden Markov Models (GA-HMM) allows HMMs with different numbers of states to evolve. 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 GA-HMM was capable of finding an HMM comparable to a hand-coded HMM designed for the same task, which has previously been published.

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Published date: December 2004
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 259393
URI: https://eprints.soton.ac.uk/id/eprint/259393
ISSN: 1367-4803
PURE UUID: 8450b8cc-0c54-4ae3-a7c6-5eb3e48700be

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Date deposited: 26 Jan 2005
Last modified: 28 Aug 2019 19:02

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Contributors

Author: K.-J. Won
Author: A. Prugel-Bennett
Author: A. Krogh

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