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Evolving hidden Markov models for protein secondary structure prediction

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4 Author(s)
K. -J. Won ; Sch. of Electron. & Comput. Sci., Southampton Univ., UK ; T. Hamelryck ; A. Prugel-Bennett ; A. Krogh

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

Published in:

2005 IEEE Congress on Evolutionary Computation  (Volume:1 )

Date of Conference:

5-5 Sept. 2005