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The General HiddenMarkov Model Library (GHMM) is a C library with additional Python bindings implementing a wide range of types of HiddenMarkov Models and algorithms: discrete, continous emissions, basic training, HMM clustering, HMM mixtures.
TransGeneScan is a gene finding tool for metatranscriptomic sequences
...TransGeneScan is no longer maintained in SourceForge. Please find the latest version in Github.
TransGeneScan is a gene finding tool for Metatranscriptomic sequences. TransGeneScan incorporates strand-specic hidden states, representing coding sequences in sense and anti-sense strands on transcripts in a HiddenMarkov Model similar to the one used in FragGeneScan (http://fraggenescan.sourceforge.net/), and can predict a sense transcript containing one or multiple genes (in an operon) or an antisense transcript.
The AK toolkit is another kit for building and use HiddenMarkov Models (HMMs). Originally developed for handwritten text recognition (HTR) using Bernoulli HMMs, it also implements diagonal Gaussians and can be used for any other purpose.
ESMERALDA is a development environment for statistical
recognizers operating on sequential data (speech, handwriting,
biological sequences). It supports continuous density HiddenMarkov models, Markov chain (N-gramm) models, and Gaussian
mixture models.
CMATLIB is set of libraries for writing numerical applicatons. It
includes support for neural-networks, hiddenMarkov models, kd-trees,
and data smoothing. It may be used from C and Scheme programs.