Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/29231
Title: Discriminative mining of gene microarray data
Authors: Lu, J
Wang, Y
Xuan, J
Kung, SY
Clarke, R
Wang Z
Gu, Z
Keywords: Biocomputing
Data mining
Evolutionary computation
Issue Date: 2001
Publisher: IEEE
Source: Proceedings of the 2001 IEEE Signal Processing Society Workshop Neural Networks for Signal Processing XI, 2001, September 2001, North Falmouth, MA, p. 23-32 How to cite?
Abstract: Spotted cDNA microarrays are emerging as a cost effective tool for the large scale analysis of gene expression. To reveal the patterns of genes expressed within a specific cell essentially responsible for its phenotype, this paper reports our progress in cluster discovery using a newly developed data mining method. The discussion entails: (1) statistical modeling of gene microarray data with a standard finite normal mixture distribution, (2) development of a joint supervised and unsupervised discriminative mining to discover sample clusters in a visual pyramid, and (3) evaluation of the data clusters produced by such scheme with phenotype-known microarray experiments
URI: http://hdl.handle.net/10397/29231
ISBN: 0-7803-7196-8
ISSN: 1089-3555
DOI: 10.1109/NNSP.2001.943107
Appears in Collections:Conference Paper

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