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3 May 1999 Clustering analysis for gene expression data
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Proceedings Volume 3602, Advances in Fluorescence Sensing Technology IV; (1999)
Event: BiOS '99 International Biomedical Optics Symposium, 1999, San Jose, CA, United States
The recent development of cDNA microarray allows ready access to large amount gene expression patterns for many genetic materials. Gene expression of tissue samples can be quantitatively analyzed by hybridizing fluor-tagged mRNA to targets on a cDNA microarray. Ratios of average expression level arising from co-hybridized normal and pathological samples are extracted via image segmentation, thus the gene expression pattern are obtained. The gene expression in a given biological process may provide a fingerprint of the sample development, or response to certain treatment. We propose a K-mean based algorithm in which gene expression levels fluctuate in parallel will be clustered together. The resulting cluster suggests some functional relationships between genes, and some known genes belongs to a unique functional classes shall provide indication for unknown genes in the same clusters.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yidong Chen, Olga Ermolaeva, Michael L. Bittner, Paul S. Meltzer, Jeffrey M. Trent, Edward R. Dougherty, and Sinan Batman "Clustering analysis for gene expression data", Proc. SPIE 3602, Advances in Fluorescence Sensing Technology IV, (3 May 1999);

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