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
Current Bioinformatics
Title: Hidden Markov Models in Bioinformatics
Volume: 2 Issue: 1
Author(s): Valeria De Fonzo, Filippo Aluffi-Pentini and Valerio Parisi
Affiliation:
Keywords: Hidden markov model, HMM, dynamical programming, labeling, sequence profiling, structure prediction
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.
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Cite this article as:
De Fonzo Valeria, Aluffi-Pentini Filippo and Parisi Valerio, Hidden Markov Models in Bioinformatics, Current Bioinformatics 2007; 2 (1) . https://dx.doi.org/10.2174/157489307779314348
DOI https://dx.doi.org/10.2174/157489307779314348 |
Print ISSN 1574-8936 |
Publisher Name Bentham Science Publisher |
Online ISSN 2212-392X |
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