Evolving Hidden Markov Models for protein secondary structure prediction

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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.

Original languageEnglish
Title of host publication2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
Number of pages8
Volume1
Publication date31 Oct 2005
Pages33-40
ISBN (Print)0780393635, 9780780393639
Publication statusPublished - 31 Oct 2005
Event2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 - Edinburgh, Scotland, United Kingdom
Duration: 2 Sep 20055 Sep 2005

Conference

Conference2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005
LandUnited Kingdom
ByEdinburgh, Scotland
Periode02/09/200505/09/2005

ID: 199873169