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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 |
| 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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