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, Edinburgh, 02 - 05 Sep 2005. , 33-40.


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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 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 (Speech)
Additional Information: Event Dates: September 2-5
ISBNs: 0780393643
Keywords: block HMM, protein secondary structure, genetic algorithm
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
ePrint ID: 261195
Date Deposited: 06 Sep 2005
Last Modified: 27 Mar 2014 20:04
Further Information:Google Scholar
ISI Citation Count:1
URI: http://eprints.soton.ac.uk/id/eprint/261195

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