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Computation of mutual information from Hidden Markov Models

Paper ID Volume ID Publish Year Pages File Format Full-Text
15289 1400 2010 6 PDF Available
Title
Computation of mutual information from Hidden Markov Models
Abstract

Understanding evolution at the sequence level is one of the major research visions of bioinformatics. To this end, several abstract models – such as Hidden Markov Models – and several quantitative measures – such as the mutual information – have been introduced, thoroughly investigated, and applied to several concrete studies in molecular biology. With this contribution we want to undertake a first step to merge these approaches (models and measures) for easy and immediate computation, e.g. for a database of a large number of externally fitted models (such as PFAM). Being able to compute such measures is of paramount importance in data mining, model development, and model comparison. Here we describe how one can efficiently compute the mutual information of a homogenous Hidden Markov Model orders of magnitude faster than with a naive, straight-forward approach. In addition, our algorithm avoids sampling issues of real-world sequences, thus allowing for direct comparison of various models. We applied the method to genomic sequences and discuss properties as well as convergence issues.

Keywords
Hidden Markov Model; Mutual information; Dynamic; Programming; Co-evolutionary signals
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Computation of mutual information from Hidden Markov Models
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 34, Issues 5–6, December 2010, Pages 328–333
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us