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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.
RefineHMM refines an original hiddenMarkov model (HMM) to find an optimal fit
against the evolutionary group that the HMM models, and it does this using
through iterative database searches and incremental subsequent adaptation of
the seed set.
D-finder is a bioinformatic search algorithm for the identification of D-sites in JNK interacting proteins. The algorithm is a combination of pattern matching and a hiddenmarkov model (HMM) based on a training set of known JNK D-sites.
Conrad is both a high performance Conditional Random Field engine which can be applied to a variety of machine learning problems and a specific set of models for gene prediction using semi-Markov CRFs.
HmmSDK is a hiddenMarkov model (HMM) software development kit written in Java. It consists of core library of HMM functions (Forward-backward, Viterbi, and Baum-Welch algorithms) and toolkits for application development.