Substitution matrices and hidden Markov models.
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.
Division of Biology and Jet Propulsion Laboratory, California Institute of Technology, Pasadena 91125, USA.
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D000465:Algorithms; D008390:Markov Chains; D015233:Models, Statistical; D011506:Proteins / Q000235:genetics; D016415:Sequence Alignment / Q000706:statistics & numerical data*
D011506:Proteins