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Evolving Hidden Markov Models for Protein Secondary Structure Prediction

Evolving Hidden Markov Models for Protein Secondary Structure Prediction
Evolving Hidden Markov Models for Protein Secondary Structure Prediction
New results are presented for the prediction of secondary structure information for protein sequences using Hidden Markov Models (HMMs) evolved using a Genetic Algorithm (GA). We achieved a Q3 measure of 75% using one of the most stringent data set ever used for protein secondary structure prediction. Our results beat the best hand-designed HMM currently available and are comparable to the best known techniques for this problem. A hybrid GA incorporating the Baum-Welch algorithm was used. The topology of the HMM was restricted to biologically meaningful building blocks. Mutation and crossover operators were designed to explore this space of topologies.
block HMM, protein secondary structure, genetic algorithm
0-7803-9364-3
33-40
Won, Kyoung Jae
3e4dfff9-c3c9-407d-8dfa-63be6f65485b
Hamelryck, Thomas
7caaa3c9-9744-4d42-a574-96e79e88373a
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e
Krogh, Anders
4a5cfccf-5403-45fb-96c5-61f60bef7d79
Won, Kyoung Jae
3e4dfff9-c3c9-407d-8dfa-63be6f65485b
Hamelryck, Thomas
7caaa3c9-9744-4d42-a574-96e79e88373a
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e
Krogh, Anders
4a5cfccf-5403-45fb-96c5-61f60bef7d79

Won, Kyoung Jae, Hamelryck, Thomas, Prugel-Bennett, Adam and Krogh, Anders (2005) Evolving Hidden Markov Models for Protein Secondary Structure Prediction. IEEE Congress on Evolutionary Computation. 02 - 05 Sep 2005. pp. 33-40 .

Record type: Conference or Workshop Item (Other)

Abstract

New results are presented for the prediction of secondary structure information for protein sequences using Hidden Markov Models (HMMs) evolved using a Genetic Algorithm (GA). We achieved a Q3 measure of 75% using one of the most stringent data set ever used for protein secondary structure prediction. Our results beat the best hand-designed HMM currently available and are comparable to the best known techniques for this problem. A hybrid GA incorporating the Baum-Welch algorithm was used. The topology of the HMM was restricted to biologically meaningful building blocks. Mutation and crossover operators were designed to explore this space of topologies.

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More information

Published date: 2005
Additional Information: Event Dates: September 2-5
Venue - Dates: IEEE Congress on Evolutionary Computation, 2005-09-02 - 2005-09-05
Keywords: block HMM, protein secondary structure, genetic algorithm
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 261195
URI: https://eprints.soton.ac.uk/id/eprint/261195
ISBN: 0-7803-9364-3
PURE UUID: 1c82f61d-8457-40f6-bae8-ca04386798df

Catalogue record

Date deposited: 06 Sep 2005
Last modified: 18 Jul 2017 09:04

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