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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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Example of a simple DTree over features (XA, XB, XC, XD). For this tree, we have the following joint distribution P(xA, xB, xC, xD) = P (xA)P (xB/xA)P (xC/xB)P (xD/xB).
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Figure 2: Example of a simple DTree over features (XA, XB, XC, XD). For this tree, we have the following joint distribution P(xA, xB, xC, xD) = P (xA)P (xB/xA)P (xC/xB)P (xD/xB).

Mentions: where P(·/·, θ) is a conditional distribution, such as conditional Gaussians [19], and θjk are the parameters of the conditional distribution. See Figure 2 for an example of a DTree and its distribution.


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

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

Example of a simple DTree over features (XA, XB, XC, XD). For this tree, we have the following joint distribution P(xA, xB, xC, xD) = P (xA)P (xB/xA)P (xC/xB)P (xD/xB).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Example of a simple DTree over features (XA, XB, XC, XD). For this tree, we have the following joint distribution P(xA, xB, xC, xD) = P (xA)P (xB/xA)P (xC/xB)P (xD/xB).
Mentions: where P(·/·, θ) is a conditional distribution, such as conditional Gaussians [19], and θjk are the parameters of the conditional distribution. See Figure 2 for an example of a DTree and its distribution.

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