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

• Published in: BMC bioinformatics Vol. 11; no. 1; p. 9
• Main Authors: Georgi, Benjamin, Costa, Ivan Gesteira, Schliep, Alexander
• Format: Journal Article
• Language: English
• Published: London BioMed Central 06.01.2010; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Applications software; Automated; Bioinformatics; Bioinformatics (Computational Biology); Bioinformatik (beräkningsbiologi); Biological analysis; Biomedical and Life Sciences; Cell cycle; Cluster Analysis; Comparative analysis; Computational Biology; Computational Biology - methods; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Databases, Genetic; DNA; Gene expression; Gene Expression Profiling; Gene Expression Profiling - methods; Genetic; Genetic algorithms; Life Sciences; Methods; Microarrays; Pattern Recognition; Pattern Recognition, Automated; Random variables; Sequence Analysis; Sequence Analysis, DNA; Software; Statistical models; Studies; United States; Dependence Tree; Pairwise Constraint; Expectation Maximization; Diagonal Covariance Matrice; Mixture Model
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-11-9

Background 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. Results 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. Conclusions 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.