Evolving hidden Markov models for protein secondary structure prediction

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Standard

Evolving hidden Markov models for protein secondary structure prediction. / Won, Kyoung Jae; Hamelryck, Thomas; Prügel-Bennett, Adam; Krogh, Anders.

The 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005: Proceedings. Bind 3 IEEE, 2005. s. 33-40.

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

Harvard

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

APA

Won, K. J., 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, s. 33-40). IEEE. https://doi.org/10.1109/CEC.2005.1554664

Vancouver

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

Author

Won, Kyoung Jae ; Hamelryck, Thomas ; Prügel-Bennett, Adam ; Krogh, Anders. / Evolving hidden Markov models for protein secondary structure prediction. The 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005: Proceedings. Bind 3 IEEE, 2005. s. 33-40

Bibtex

@inproceedings{8963cbb28805441ba8bbf726e86b35cf,
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",
year = "2005",
doi = "10.1109/CEC.2005.1554664",
language = "English",
isbn = "0-7803-9363-5",
volume = "3",
pages = "33--40",
booktitle = "The 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005",
publisher = "IEEE",
note = "2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 ; Conference date: 02-09-2005 Through 05-09-2005",

}

RIS

TY - GEN

T1 - Evolving hidden Markov models for protein secondary structure prediction

AU - Won, Kyoung Jae

AU - Hamelryck, Thomas

AU - Prügel-Bennett, Adam

AU - Krogh, Anders

PY - 2005

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.

U2 - 10.1109/CEC.2005.1554664

DO - 10.1109/CEC.2005.1554664

M3 - Article in proceedings

AN - SCOPUS:27144535913

SN - 0-7803-9363-5

VL - 3

SP - 33

EP - 40

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

PB - IEEE

T2 - 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005

Y2 - 2 September 2005 through 5 September 2005

ER -

ID: 199873169