Mixture modelling of gene expression data from microarray experiments | Zendy

Debashis Ghosh | Zendy; Arul M. Chinnaiyan | Zendy

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Mixture modelling of gene expression data from microarray experiments

Author(s) -

Debashis Ghosh,

Arul M. Chinnaiyan

Publication year - 2002

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/18.2.275

Subject(s) - cluster analysis , microarray analysis techniques , computer science , data mining , hierarchical clustering , microarray , gene chip analysis , microarray databases , probabilistic logic , computational biology , artificial intelligence , gene , gene expression , biology , genetics

Hierarchical clustering is one of the major analytical tools for gene expression data from microarray experiments. A major problem in the interpretation of the output from these procedures is assessing the reliability of the clustering results. We address this issue by developing a mixture model-based approach for the analysis of microarray data. Within this framework, we present novel algorithms for clustering genes and samples. One of the byproducts of our method is a probabilistic measure for the number of true clusters in the data.

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