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Evolving Hidden Markov Models for Protein Secondary Structure Prediction

Record type: Conference or Workshop Item (Other)

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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Citation

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

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: http://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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Contributors

Author: Kyoung Jae Won
Author: Thomas Hamelryck
Author: Adam Prugel-Bennett
Author: Anders Krogh

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