An evolutionary method for learning HMM structure: prediction of protein secondary structure |
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| Authors: | Kyoung-Jae Won Thomas Hamelryck Adam Prügel-Bennett Anders Krogh |
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| Affiliation: | (1) Department of Molecular Biology, Bioinformatics Centre, University of Copenhagen, Ole Maaloes Vej 5, DK-2200 Copenhagen, Denmark;(2) School of Electronics and Computer Science, University of Southampton, SO17 1BJ, UK;(3) Department of Chemistry & Biochemistry, UCSD, 9500 Gilman Drive, Mail Code 0359, La Jolla, CA 92093-0359, USA |
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| Abstract: | Background The prediction of the secondary structure of proteins is one of the most studied problems in bioinformatics. Despite their success in many problems of biological sequence analysis, Hidden Markov Models (HMMs) have not been used much for this problem, as the complexity of the task makes manual design of HMMs difficult. Therefore, we have developed a method for evolving the structure of HMMs automatically, using Genetic Algorithms (GAs). |
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