Hidden Markov Models in 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.

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