Abstract
ABSTRACT
Hidden Markov models (HMMs) provide a general framework for expressing primary sequence consensus. HMMs can effectively be used to model and align protein families, and to search data bases. HMMs, however, have a large number of parameters. When only few sequences are available for model fitting, additional prior information must be incorporated into the models. We derive a simple algorithm that directly incorporates prior information provided by substitution matrices into the HMM learning procedure.
Key words:
hidden Markov models, substitution matrices, PAM matrices, protein modeling, multiple alignments, data base searches
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