ArticleNucleic acids research2009
HMMCONVERTER 1.0: a toolbox for hidden Markov models.
Article in Nucleic acids research, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Machine Boss: rapid prototyping of bioinformatic automata.Bioinformatics (Oxford, England) · 2021Article
- StochHMM: a flexible hidden Markov model tool and C++ library.Bioinformatics (Oxford, England) · 2014Article
- ToPS: a framework to manipulate probabilistic models of sequence data.PLoS computational biology · 2013Article
- Efficient algorithms for training the parameters of hidden Markov models using stochastic expectation maximization (EM) training and Viterbi training.Algorithms for molecular biology : AMB · 2010Article
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Authors and funding
2 authors.
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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.
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