Evolving Hidden Markov Models for Protein Secondary Structure Prediction

Won, Kyoung Jae, Hamelryck, Thomas, Prugel-Bennett, Adam and Krogh, Anders (2005) Evolving Hidden Markov Models for Protein Secondary Structure Prediction At IEEE Congress on Evolutionary Computation. 02 - 05 Sep 2005. , pp. 33-40.


[img] PDF 77.pdf - Other
Download (158kB)


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.

Item Type: Conference or Workshop Item (Other)
Additional Information: Event Dates: September 2-5
Venue - Dates: IEEE Congress on Evolutionary Computation, 2005-09-02 - 2005-09-05
Keywords: block HMM, protein secondary structure, genetic algorithm
Organisations: Southampton Wireless Group
ePrint ID: 261195
Date :
Date Event
Date Deposited: 06 Sep 2005
Last Modified: 17 Apr 2017 22:00
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/261195

Actions (login required)

View Item View Item