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Hidden Markov Models in Bioinformatics

[ Vol. 2 , Issue. 1 ]


Valeria De Fonzo, Filippo Aluffi-Pentini and Valerio Parisi   Pages 49 - 61 ( 13 )


Hidden Markov Models (HMMs) became recently important and popular among bioinformatics researchers, and many software tools are based on them. In this survey, we first consider in some detail the mathematical foundations of HMMs, we describe the most important algorithms, and provide useful comparisons, pointing out advantages and drawbacks. We then consider the major bioinformatics applications, such as alignment, labeling, and profiling of sequences, protein structure prediction, and pattern recognition. We finally provide a critical appraisal of the use and perspectives of HMMs in bioinformatics.


Hidden markov model, HMM, dynamical programming, labeling, sequence profiling, structure prediction


Dipartimento di Medicina Sperimentale e Patologia, Universit√† di Roma “La Sapienza”, Viale Regina Elena 324, 00161 Roma, Italy.

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