An evolutionary method for learning HMM structure: prediction of protein secondary structure.

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

An evolutionary method for learning HMM structure: prediction of protein secondary structure. / Won, Kyoung-Jae; Hamelryck, Thomas; Prügel-Bennett, Adam; Krogh, Anders.

In: BMC Bioinformatics, Vol. 8, 2007, p. 357.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Won, K-J, Hamelryck, T, Prügel-Bennett, A & Krogh, A 2007, 'An evolutionary method for learning HMM structure: prediction of protein secondary structure.', BMC Bioinformatics, vol. 8, pp. 357. https://doi.org/10.1186/1471-2105-8-357

APA

Won, K-J., Hamelryck, T., Prügel-Bennett, A., & Krogh, A. (2007). An evolutionary method for learning HMM structure: prediction of protein secondary structure. BMC Bioinformatics, 8, 357. https://doi.org/10.1186/1471-2105-8-357

Vancouver

Won K-J, Hamelryck T, Prügel-Bennett A, Krogh A. An evolutionary method for learning HMM structure: prediction of protein secondary structure. BMC Bioinformatics. 2007;8:357. https://doi.org/10.1186/1471-2105-8-357

Author

Won, Kyoung-Jae ; Hamelryck, Thomas ; Prügel-Bennett, Adam ; Krogh, Anders. / An evolutionary method for learning HMM structure: prediction of protein secondary structure. In: BMC Bioinformatics. 2007 ; Vol. 8. pp. 357.

Bibtex

@article{59ab3e00dadb11dcbee902004c4f4f50,
title = "An evolutionary method for learning HMM structure: prediction of protein secondary structure.",
abstract = "Therefore, we have developed a method for evolving the structure of HMMs automatically, using Genetic Algorithms (GAs). RESULTS: In the GA procedure, populations of HMMs are assembled from biologically meaningful building blocks. Mutation and crossover operators were designed to explore the space of such Block-HMMs. After each step of the GA, the standard HMM estimation algorithm (the Baum-Welch algorithm) was used to update model parameters. The final HMM captures several features of protein sequence and structure, with its own HMM grammar. In contrast to neural network based predictors, the evolved HMM also calculates the probabilities associated with the predictions. We carefully examined the performance of the HMM based predictor, both under the multiple- and single-sequence",
author = "Kyoung-Jae Won and Thomas Hamelryck and Adam Pr{\"u}gel-Bennett and Anders Krogh",
note = "Keywords: Algorithms; Amino Acid Sequence; Computer Simulation; Markov Chains; Models, Chemical; Models, Molecular; Molecular Sequence Data; Protein Structure, Secondary; Proteins; Sequence Analysis, Protein",
year = "2007",
doi = "10.1186/1471-2105-8-357",
language = "English",
volume = "8",
pages = "357",
journal = "B M C Bioinformatics",
issn = "1471-2105",
publisher = "BioMed Central Ltd.",

}

RIS

TY - JOUR

T1 - An evolutionary method for learning HMM structure: prediction of protein secondary structure.

AU - Won, Kyoung-Jae

AU - Hamelryck, Thomas

AU - Prügel-Bennett, Adam

AU - Krogh, Anders

N1 - Keywords: Algorithms; Amino Acid Sequence; Computer Simulation; Markov Chains; Models, Chemical; Models, Molecular; Molecular Sequence Data; Protein Structure, Secondary; Proteins; Sequence Analysis, Protein

PY - 2007

Y1 - 2007

N2 - Therefore, we have developed a method for evolving the structure of HMMs automatically, using Genetic Algorithms (GAs). RESULTS: In the GA procedure, populations of HMMs are assembled from biologically meaningful building blocks. Mutation and crossover operators were designed to explore the space of such Block-HMMs. After each step of the GA, the standard HMM estimation algorithm (the Baum-Welch algorithm) was used to update model parameters. The final HMM captures several features of protein sequence and structure, with its own HMM grammar. In contrast to neural network based predictors, the evolved HMM also calculates the probabilities associated with the predictions. We carefully examined the performance of the HMM based predictor, both under the multiple- and single-sequence

AB - Therefore, we have developed a method for evolving the structure of HMMs automatically, using Genetic Algorithms (GAs). RESULTS: In the GA procedure, populations of HMMs are assembled from biologically meaningful building blocks. Mutation and crossover operators were designed to explore the space of such Block-HMMs. After each step of the GA, the standard HMM estimation algorithm (the Baum-Welch algorithm) was used to update model parameters. The final HMM captures several features of protein sequence and structure, with its own HMM grammar. In contrast to neural network based predictors, the evolved HMM also calculates the probabilities associated with the predictions. We carefully examined the performance of the HMM based predictor, both under the multiple- and single-sequence

U2 - 10.1186/1471-2105-8-357

DO - 10.1186/1471-2105-8-357

M3 - Journal article

C2 - 17888163

VL - 8

SP - 357

JO - B M C Bioinformatics

JF - B M C Bioinformatics

SN - 1471-2105

ER -

ID: 2736988