Won, Kyoung Jae, Hamelryck, Thomas, Prugel-Bennett, Adam and Krogh, Anders
Evolving Hidden Markov Models for Protein Secondary Structure Prediction
At IEEE Congress on Evolutionary Computation.
02 - 05 Sep 2005.
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.
Conference or Workshop Item
||Event Dates: September 2-5
|Venue - Dates:
||IEEE Congress on Evolutionary Computation, 2005-09-02 - 2005-09-05
||block HMM, protein secondary structure, genetic algorithm
||Southampton Wireless Group
||06 Sep 2005
||17 Apr 2017 22:00
|Further Information:||Google Scholar|
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