Hidden Markov Models in Bioinformatics

Authors: De Fonzo, Valeria; Aluffi-Pentini, Filippo; Parisi, Valerio

Source: Current Bioinformatics, Volume 2, Number 1, January 2007 , pp. 49-61(13)

Publisher: Bentham Science Publishers

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Abstract:

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.

Keywords: Hidden markov model; HMM; dynamical programming; labeling; sequence profiling; structure prediction

Document Type: Research article

DOI: http://dx.doi.org/10.2174/157489307779314348

Affiliations: 1: Dipartimento di Medicina Sperimentale e Patologia, Università di Roma “La Sapienza”, Viale Regina Elena 324, 00161 Roma, Italy.

Publication date: 2007-01-01

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Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth reviews written by leaders in the field, covering a wide range of the integration of biology with computer and information science.

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Current Bioinformatics is an essential journal for all academic and industrial researchers who want expert knowledge on all major advances in bioinformatics.

Hidden Markov Models in Bioinformatics

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