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Bioinformatics Advance Access published online on August 5, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth454
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received October 23, 2003
Revised July 11, 2004
Accepted July 23, 2004

Applications note

Training HMM structure with genetic algorithm for biological sequence analysis

Kyoung-Jae Won 1*, Adam Prügel-Bennett 2, Anders Krogh 3

1 ISIS Group, ECS, University of Southampton, SO17 1BJ, United Kingdom
2 ISIS Group, ECS, University of Southampton, SO17 1BJ, United Kingdom, Denmark
3 Bioinformatics Centre, University of Copenhagen, DK-2100 Copenhagen, Denmark

* To whom correspondence should be addressed. E-mail: j.won{at}ecs.soton.ac.uk.


  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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