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Proceedings Paper

Automated analysis of multivariate nonlinear gene relations based on cDNA microarray expression data
Author(s): Seungchan Kim; Edward R. Dougherty; Michael L. Bittner; Yidong Chen; Krishnamoorthy Sivakumar; Paul S. Meltzer; Jeffrey M. Trent
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Paper Abstract

A cDNA microarray is a complex biochemical-optical system whose purpose is the simultaneous measurement of gene expression for thousands of genes. This paper describes a general statistical environment for finding associations among gene expression patterns, and between genes and external conditions, via the coefficient of determination. This coefficient measures the degree to which the transcriptional levels of an observed gene set can be used to improve the prediction of the transcriptional state of a target gene relative to the best possible prediction in the absence of observations. Various aspects of the method are discussed: prediction quantification, deign of predictors given small numbers of replicated microarrays, and constrained prediction using ternary perceptrons. A main focus is the supporting software and its facilities for data analysis and visualization.

Paper Details

Date Published: 22 March 2000
PDF: 6 pages
Proc. SPIE 3926, Advances in Nucleic Acid and Protein Analyses, Manipulation, and Sequencing, (22 March 2000); doi: 10.1117/12.380506
Show Author Affiliations
Seungchan Kim, Texas A&M Univ. (United States)
Edward R. Dougherty, Texas A&M Univ. (United States)
Michael L. Bittner, National Institutes of Health (United States)
Yidong Chen, National Institutes of Health (United States)
Krishnamoorthy Sivakumar, Washington State Univ. (United States)
Paul S. Meltzer, National Institutes of Health (United States)
Jeffrey M. Trent, National Institutes of Health (United States)


Published in SPIE Proceedings Vol. 3926:
Advances in Nucleic Acid and Protein Analyses, Manipulation, and Sequencing
Patrick A. Limbach; John C. Owicki; Ramesh Raghavachari; Weihong Tan, Editor(s)

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