An evolutionary method for learning HMM structure: prediction of protein secondary structure.

Research output: Contribution to journalJournal articleResearchpeer-review

Therefore, we have developed a method for evolving the structure of HMMs automatically, using Genetic Algorithms (GAs). RESULTS: In the GA procedure, populations of HMMs are assembled from biologically meaningful building blocks. Mutation and crossover operators were designed to explore the space of such Block-HMMs. After each step of the GA, the standard HMM estimation algorithm (the Baum-Welch algorithm) was used to update model parameters. The final HMM captures several features of protein sequence and structure, with its own HMM grammar. In contrast to neural network based predictors, the evolved HMM also calculates the probabilities associated with the predictions. We carefully examined the performance of the HMM based predictor, both under the multiple- and single-sequence
Original languageEnglish
JournalBMC Bioinformatics
Pages (from-to)357
Publication statusPublished - 2007

Bibliographical note

Keywords: Algorithms; Amino Acid Sequence; Computer Simulation; Markov Chains; Models, Chemical; Models, Molecular; Molecular Sequence Data; Protein Structure, Secondary; Proteins; Sequence Analysis, Protein

ID: 2736988