An evolutionary method for learning HMM structure: prediction of protein secondary structure.
Research output: Contribution to journal › Journal article › Research › peer-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 language | English |
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Journal | BMC Bioinformatics |
Volume | 8 |
Pages (from-to) | 357 |
ISSN | 1471-2105 |
DOIs | |
Publication status | Published - 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