Evolving hidden Markov models for protein secondary structure prediction

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.

• Type: Conference
• Year: 2005
• Where: CEC
• Authors: Kyoung-Jae Won, Thomas Hamelryck, Adam Prügel-Bennett, Anders Krogh
• Keywords: Artificial Intelligence, CEC 2005, Secondary Structure, Secondary Structure Information, Secondary Structure Prediction