Evolving Hidden Markov Models for protein secondary structure prediction

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 - 2005/10/31

Y1 - 2005/10/31

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.

UR - http://www.scopus.com/inward/record.url?scp=27144535913&partnerID=8YFLogxK

M3 - Article in proceedings

AN - SCOPUS:27144535913

SN - 0780393635

SN - 9780780393639

VL - 1

SP - 33

EP - 40

BT - 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings

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

Y2 - 2 September 2005 through 5 September 2005

ER -