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PyMix: The Python mixture package

The Python Mixture Package (PyMix) is a freely available Python library implementing algorithms and data structures for a wide variety of data mining applications with basic and extended mixture models.


For more downloading the most recent version, documentation and the Pymix mailing list refer to the Pymix home page.


Georgi, B. and Schliep, A.. Partially-supervised context-specific independence mixture modeling (2007) [details]

Georgi, Benjamin and Schliep, Alexander. Context-specific independence mixture modeling for positional weight matrices (2006) [details]

Georgi, Benjamin. Context-specific Independence Mixture Models for Cluster Analysis of Biological Data (2009) [details]

Costa, Ivan G. and Roepcke, Stefan and Hafemeister, Christoph and Schliep, Alexander. Inferring differentiation pathways from gene expression (2008) [details]

Georgi, Benjamin. Mixture Modeling and Group Inference in Fused Genotype and Phenotype Data (2005) [details]

Georgi, Benjamin and Gesteira Costa, Ivan and Schliep, Ivan. PyMix - The Python mixture package - a tool for clustering of heterogeneous biological data (2010) [details]

Costa, Ivan G. and Schönhuth, Alexander and Hafemeister, Christoph and Schliep, Alexander. Constrained Mixture Estimation for Analysis and Robust Classification of Clinical Time Series (2009) [details]

Georgi, Benjamin and Schultz, Jörg and Schliep, Alexander. Partially-supervised protein subclass discovery with simultaneous annotation of functional residues (2009) [details]

Georgi, B. and Spence, M. A. and Flodman, P. and Schliep, A.. Mixture model based group inference in fused genotype and phenotype data (2007) [details]

Georgi, Benjamin and Schultz, Jörg and Schliep, Alexander. Context-Specific Independence Mixture Modelling for Protein Families (2007) [details]