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PyMix--the python mixture package--a tool for clustering of heterogeneous biological data.

Georgi B, Costa IG, Schliep A - BMC Bioinformatics (2010)

Bottom Line: 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 has been successfully used for the analysis of biological sequence, complex disease and gene expression data.Due to the general nature of the framework, PyMix can be applied to a wide range of applications and data sets.

View Article: PubMed Central - HTML - PubMed

Affiliation: Max Planck Institute for Molecular Genetics, Dept, of Computational Molecular Biology, Ihnestrasse 73, 14195 Berlin. bgeorgi@mail.med.upenn.edu

ABSTRACT

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.

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Assuming data comes from a two-dimensional space, the addition of positive pairwise constraints, depicted as red edges, and negative constraints depicted as blue edges (right figure), support the existence of two or more clusters and indicate possible cluster boundaries (green lines). (Figure reproduced from [29])
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Figure 3: Assuming data comes from a two-dimensional space, the addition of positive pairwise constraints, depicted as red edges, and negative constraints depicted as blue edges (right figure), support the existence of two or more clusters and indicate possible cluster boundaries (green lines). (Figure reproduced from [29])

Mentions: The main idea behind the semi-supervised method implemented in Pymix is to find a clustering solution Y where the least number of constraints are violated [22] (see Figure 3). This can be achieved by redefining the posterior assignment rule of the EM (Eq. 3), as


PyMix--the python mixture package--a tool for clustering of heterogeneous biological data.

Georgi B, Costa IG, Schliep A - BMC Bioinformatics (2010)

Assuming data comes from a two-dimensional space, the addition of positive pairwise constraints, depicted as red edges, and negative constraints depicted as blue edges (right figure), support the existence of two or more clusters and indicate possible cluster boundaries (green lines). (Figure reproduced from [29])
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2823712&req=5

Figure 3: Assuming data comes from a two-dimensional space, the addition of positive pairwise constraints, depicted as red edges, and negative constraints depicted as blue edges (right figure), support the existence of two or more clusters and indicate possible cluster boundaries (green lines). (Figure reproduced from [29])
Mentions: The main idea behind the semi-supervised method implemented in Pymix is to find a clustering solution Y where the least number of constraints are violated [22] (see Figure 3). This can be achieved by redefining the posterior assignment rule of the EM (Eq. 3), as

Bottom Line: 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 has been successfully used for the analysis of biological sequence, complex disease and gene expression data.Due to the general nature of the framework, PyMix can be applied to a wide range of applications and data sets.

View Article: PubMed Central - HTML - PubMed

Affiliation: Max Planck Institute for Molecular Genetics, Dept, of Computational Molecular Biology, Ihnestrasse 73, 14195 Berlin. bgeorgi@mail.med.upenn.edu

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus