ECS Research
Publications
Intranet Tools
RSS 1.0 Feed
RSS 2.0 Feed
Atom Feed
 

The Block Hidden Markov Model for Biological Sequence Analysis

Won, K. J., Prugel-Bennett, A. and Krogh, A. (2004) The Block Hidden Markov Model for Biological Sequence Analysis. Lecture Notes in Artificial Intelligence, Knowledge-Based Intelligent Information and Engineering Systems: 8th International Conference, KES 2004, Wellington, New Zealand, September 20-25, 2004, 3213 . pp. 64-70. ISSN 0302-9743

Full text not available from this repository.

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.

Creators:Kyoung-Jae Won, Adam Prugel-Bennett, Anders Krogh
Editors:Mircea Gh Mircea Gh, Robert J. Howlett, Lakhmi C. Jain
Item Type:Article
Research Group:Information - Signals, Images, Systems
Deposited On:30 Mar 2005 by Won, Kyoung Jae
Alternative Locations:http://www.springerlink.com/index/1LAB54RC3EJT754X
ISSN:0302-9743
ID Code:10704
Last Modified:11 Nov 2009 12:22
Performance Indicator:EZ~03~02~11
Citations:ISI: 2, Google Scholar: 1

Tools

Metadata

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in this archive you will be forwarded to the paracite service. Poorly formated references will probably not work.

1. Durbin, R., Eddy, S., Krogh, A., Mitchison, G.: Biological sequence analysis. Cambridge. Cambridge University Press (1998)

2. Petersen, L., Larsen, T.S., Ussery, D.W., On, S.L.W., Krogh, A.: Rpod promoters in Cam-pylobacter jejuni exhibit a strong periodic signal instead of a -35 box. In: Journal of Molecular Biology, 326(5):1361-1372. (2003)

3. Krogh, A., Larsson, B., von Heijne, G., Sonnhammer, E.: Predicting transmembrane protein topology with a Hidden Markov Model: Application to complete genomes. In: Journal of Molecular Biology, 305(3):567-580. (2003)

4. Goldberg, D.E.: Genetic Algorithms in Search, Optimization & Machine Learning. Addi-son-Wesley (Reading, Mass) (1989)

5. T.Yada, M.Ishikawa, H.Tanaka, K.Asai: DNA Sequence Analysis Using Hidden Markov Model and Genetic Algorithm. In: Genome Informatics Vol.5, pp.178-179. (1994)

6. Baker, J.E.: Reducing bias and inefficiency in the selection algorithm. In: Proceedings of the Second International Conference on Genetic Algorithms, Lawrence Erlbaum Associates (Hillsdale) (1987)

7. Prügel-Bennett, A., Shapiro, J.L.: An analysis of genetic algorithms using statistical me-chanics. In: Physical Review Letters, 72(9):1305-1309. (1994)

Corrections

ECS staff and postgraduates may modify this record

   Welcome from Deputy Head of School (Research) Research Prospectus Industrial Partnerships New Research Students Notes for Guidance New Research Students Notes for Guidance
The ECS EPrints Repository supports OAI 2.0 with a base URL of http://eprints.ecs.soton.ac.uk/cgi/oai2

EPrints is free software developed by the University of Southampton to facilitate Open Access to research.		 EPrints