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GHMM: General Hidden Markov Model library

The General Hidden Markov Model library (GHMM) is a freely available LGPL-ed C library implementing efficient data structures and algorithms for basic and extended HMMs. The development is hosted at Sourceforge http://sourceforge.net/projects/ghmm/, where you have access to the Subversion repository, mailing lists and forums.

Features

HMMEd (the Hidden Markov Model editor) is a graphical application which allows to create and edit Hidden Markov Models. Supported are

Discrete emission probabilities, as well as transition probabilities can be graphically edited using pie charts with handles. Parameters can also be entered directly. HMM topology can be edited manually by adding/deleting states and connecting them with the mouse. Hierarchical models, i.e. super nodes corresponding to complete promoters or codon models in gene finding HMMs, will be supported. Currently, a visually pleasant layout has to be done by hand.

For models with continous emission, a graphical editor for mixtures of pdfs is under development. A screenshot from a working prototype is shown above. The handles underneath the pdfs change mean as well as other pdf parameters, the pie chart controls the mixture coefficients.

HMMEd is licensed under the GPL. Its based on code from Gato the graph animation toolbox. Note: HMMEd can be found in the Gato subdirectory in the CVS [Browse Sourceforge CVS]

Publications

Costa, Ivan G. and Schönhuth, Alexander and Schliep, Alexander. The Graphical Query Language: a tool for analysis of gene expression time-courses (2005) [details]

Knab, Bernhard. Extension of Hidden Markov Models for the analysis of financial time-series data (2000) [details]

Knab, Bernhard and Schliep, Alexander and Steckemetz, Barthel and Wichern, Bernd. Model-Based Clustering With Hidden Markov Models and its Application to Financial Time-Series Data (2003) [details]

Schliep, Alexander and Costa, Ivan G. and Steinhoff, Christine and Schönhuth, Alexander. Analyzing gene expression time-courses (2005) [details]

Schliep, Alexander and Schönhuth, Alexander and Steinhoff, Christine. Using hidden Markov models to analyze gene expression time course data (2003) [details]

Schliep, Alexander and Steinhoff, Christine and Schönhuth, Alexander. Robust inference of groups in gene expression time-courses using mixtures of HMMs (2004) [details]

Wichern, Bernd. Hidden Markov Models for the analysis of data from saving and loan banks (2001) [details]

Costa, Ivan G.. Mixture Models for the Analysis of Gene Expression: Integration of Multiple Experiments and Cluster Validation (2008) [details]

Weisse, Andrea. Recognition of Circular Permutations in Proteins with Hidden Markov Models (2003) [details]

Georgi, Benjamin. A Graph-Based Approach to Clustering of Profile Hidden Markov Models (2003) [details]

Riemer, Alexander. Chromosome-wide Expression for Improving ab-initio Gene Prediction (2004) [details]

Grunau, Janne. Discriminative Learning in Hidden Markov Models (2004) [details]

Schliep, Alexander and Georgi, Benjamin and Rungsarityotin, Wasinee and Costa, Ivan G. and Schönhuth, Alexander. The General Hidden Markov Model Library: Analyzing Systems with Unobservable States (2005) [details]

Schönhuth, Alexander and Costa, Ivan G. and Schliep, Alexander. Semi-supervised Clustering of Yeast Gene Expression (2009) [details]

Costa, I. G. and Krause, R. and Optiz, L. and Schliep, A.. Semi-supervised learning for the identification of syn-expressed genes from fused microarray and in situ image data (2007) [details]