Evolving hidden Markov models for protein secondary structure prediction

Author

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

Author_Institution

Sch. of Electron. & Comput. Sci., Southampton Univ.

Volume

1

fYear

2005

fDate

5-5 Sept. 2005

Firstpage

33

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

Keywords

biology computing; genetic algorithms; hidden Markov models; proteins; HMM; crossover operators; genetic algorithm; hidden Markov models; hybrid GA; mutation operators; protein secondary structure information prediction; protein sequences; Bioinformatics; Computer science; Genetic algorithms; Hidden Markov models; Network topology; Neural networks; Proteins; Space exploration; Support vector machines; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2005. The 2005 IEEE Congress on

Conference_Location

Edinburgh, Scotland

Print_ISBN

0-7803-9363-5

Type

conf

DOI

10.1109/CEC.2005.1554664

Filename

1554664