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Clustering gene-expression data with repeated measurements.


ABSTRACT: Clustering is a common methodology for the analysis of array data, and many research laboratories are generating array data with repeated measurements. We evaluated several clustering algorithms that incorporate repeated measurements, and show that algorithms that take advantage of repeated measurements yield more accurate and more stable clusters. In particular, we show that the infinite mixture model-based approach with a built-in error model produces superior results.

SUBMITTER: Yeung KY 

PROVIDER: S-EPMC156590 | biostudies-literature | 2003

REPOSITORIES: biostudies-literature

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Clustering gene-expression data with repeated measurements.

Yeung Ka Yee KY   Medvedovic Mario M   Bumgarner Roger E RE  

Genome biology 20030425 5


Clustering is a common methodology for the analysis of array data, and many research laboratories are generating array data with repeated measurements. We evaluated several clustering algorithms that incorporate repeated measurements, and show that algorithms that take advantage of repeated measurements yield more accurate and more stable clusters. In particular, we show that the infinite mixture model-based approach with a built-in error model produces superior results. ...[more]

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