However the basic hmm toolkit can only be stretched

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Unformatted text preview: as no way of ‘remembering’ what a distant state generated. Sometimes, one can bend the rules of HMMs without breaking the algorithms. For instance, in genefinding, one wants to emit a correlated triplet codon instead of three independent residues; HMM algorithms can readily be extended to tripletemitting states. However, the basic HMM toolkit can only be stretched so far. Beyond HMMs, there are more powerful (though less efficient) classes of probabilistic models for sequence analysis. 1. Rabiner, L.R. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77, 257–286 (1989). 2. Durbin, R., Eddy, S.R., Krogh, A. & Mitchison, G.J. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids (Cambridge University Press, Cambridge UK, 1998). j Any model that has these properties is an HMM. This means that one can make a new HMM just by drawing a picture corresponding to the problem at hand, like Figure 1. This graphical simplicity lets one focus clearly on the biological definition of a problem. Wondering how some other mathematical technique really works? Send suggestions for future primers to [email protected] VOLUME 22 NUMBER 10 OCTOBER 2004 N ATURE BIOTECHNOLOGY...
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