COMPARATIVE STUDY
EVALUATION STUDIES
JOURNAL ARTICLE
RESEARCH SUPPORT, U.S. GOV'T, NON-P.H.S.
RESEARCH SUPPORT, U.S. GOV'T, P.H.S.
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Supervised cluster analysis for microarray data based on multivariate Gaussian mixture.

Bioinformatics 2004 August 13
MOTIVATION: Grouping genes having similar expression patterns is called gene clustering, which has been proved to be a useful tool for extracting underlying biological information of gene expression data. Many clustering procedures have shown success in microarray gene clustering; most of them belong to the family of heuristic clustering algorithms. Model-based algorithms are alternative clustering algorithms, which are based on the assumption that the whole set of microarray data is a finite mixture of a certain type of distributions with different parameters. Application of the model-based algorithms to unsupervised clustering has been reported. Here, for the first time, we demonstrated the use of the model-based algorithm in supervised clustering of microarray data.

RESULTS: We applied the proposed methods to real gene expression data and simulated data. We showed that the supervised model-based algorithm is superior over the unsupervised method and the support vector machines (SVM) method.

AVAILABILITY: The program written in the SAS language implementing methods I-III in this report is available upon request. The software of SVMs is available in the website https://svm.sdsc.edu/cgi-bin/nph-SVMsubmit.cgi

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