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HMMCONVERTER 1.0: a toolbox for hidden Markov models

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Item Type:Article
Title:HMMCONVERTER 1.0: a toolbox for hidden Markov models
Creators Name:Lam, T.Y. and Meyer, I.M.
Abstract:Hidden Markov models (HMMs) and their variants are widely used in Bioinformatics applications that analyze and compare biological sequences. Designing a novel application requires the insight of a human expert to define the model's architecture. The implementation of prediction algorithms and algorithms to train the model's parameters, however, can be a time-consuming and error-prone task. We here present HMMConverter, a software package for setting up probabilistic HMMs, pair-HMMs as well as generalized HMMs and pair-HMMs. The user defines the model itself and the algorithms to be used via an XML file which is then directly translated into efficient C++ code. The software package provides linear-memory prediction algorithms, such as the Hirschberg algorithm, banding and the integration of prior probabilities and is the first to present computationally efficient linear-memory algorithms for automatic parameter training. Users of HMMConverter can thus set up complex applications with a minimum of effort and also perform parameter training and data analyses for large data sets.
Keywords:Algorithms, Markov Chains, Software, Statistical Models
Source:Nucleic Acids Research
ISSN:0305-1048
Publisher:Oxford University Press (U.K.)
Volume:37
Number:21
Page Range:e139
Date:November 2009
Official Publication:https://doi.org/10.1093/nar/gkp662
PubMed:View item in PubMed

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