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PyMix - The Python mixture package - a tool for clustering of heterogeneous biological data

Benjamin Georgi14*, Ivan Gesteira Costa2 and Alexander Schliep13

Author Affiliations

1 Max Planck Institute for Molecular Genetics, Dept. of Computational Molecular Biology, Ihnestrasse 73, 14195 Berlin

2 Center of Informatics, Federal University of Pernambuco, Recife, Brazil

3 Dept. of Computer Science and BioMaPS Institute for Quantitative Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA

4 Department of Genetics, University of Pennsylvania, 528 CRB, 415 Curie Blvd PA 19104 Philadelphia, USA

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BMC Bioinformatics 2010, 11:9  doi:10.1186/1471-2105-11-9

Published: 6 January 2010



Cluster analysis is an important technique for the exploratory analysis of biological data. Such data is often high-dimensional, inherently noisy and contains outliers. This makes clustering challenging. Mixtures are versatile and powerful statistical models which perform robustly for clustering in the presence of noise and have been successfully applied in a wide range of applications.


PyMix - the Python mixture package implements algorithms and data structures for clustering with basic and advanced mixture models. The advanced models include context-specific independence mixtures, mixtures of dependence trees and semi-supervised learning. PyMix is licenced under the GNU General Public licence (GPL). PyMix has been successfully used for the analysis of biological sequence, complex disease and gene expression data.


PyMix is a useful tool for cluster analysis of biological data. Due to the general nature of the framework, PyMix can be applied to a wide range of applications and data sets.