Evolving hidden Markov models for protein secondary structure prediction

Kyoung Jae Won; Thomas Hamelryck; Adam Prügel-Bennett; Anders Krogh

doi:10.1109/CEC.2005.1554664

Evolving hidden Markov models for protein secondary structure prediction

Kyoung Jae Won^*, Thomas Hamelryck, Adam Prügel-Bennett, Anders Krogh

^*Corresponding author af dette arbejde

Bioinformatik og RNA Biologi

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

12 Citationer (Scopus)

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 Q ₃ 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.

Originalsprog	Engelsk
Titel	The 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 : Proceedings
Antal sider	8
Vol/bind	3
Forlag	IEEE
Publikationsdato	2005
Sider	33-40
ISBN (Trykt)	0-7803-9363-5
DOI	https://doi.org/10.1109/CEC.2005.1554664
Status	Udgivet - 2005
Begivenhed	2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 - Edinburgh, Scotland, Storbritannien Varighed: 2 sep. 2005 → 5 sep. 2005

Konference

Konference	2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005
Land/Område	Storbritannien
By	Edinburgh, Scotland
Periode	02/09/2005 → 05/09/2005

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10.1109/CEC.2005.1554664

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Citationsformater

Won, KJ, Hamelryck, T, Prügel-Bennett, A & Krogh, A 2005, Evolving hidden Markov models for protein secondary structure prediction. i The 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005: Proceedings. bind 3, IEEE, s. 33-40, 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005, Edinburgh, Scotland, Storbritannien, 02/09/2005. https://doi.org/10.1109/CEC.2005.1554664

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title = "Evolving hidden Markov models for protein secondary structure prediction",

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 Q 3 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.",

author = "Won, {Kyoung Jae} and Thomas Hamelryck and Adam Pr{\"u}gel-Bennett and Anders Krogh",

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AU - Won, Kyoung Jae

AU - Hamelryck, Thomas

AU - Prügel-Bennett, Adam

AU - Krogh, Anders

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Y1 - 2005

N2 - 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 Q 3 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.

AB - 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 Q 3 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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DO - 10.1109/CEC.2005.1554664

M3 - Article in proceedings

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BT - The 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005

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