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Abstract Details

Activity Number: 681
Type: Contributed
Date/Time: Thursday, August 5, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract - #308786
Title: Tumor Class Prediction Using Gene Expression Microarray Data
Author(s): Xueli Liu*+ and Mark Yang and Jeffrey Longmate
Companies: City of Hope and University of Florida and City of Hope
Address: 1500 Duarte Road, Duarte, CA, 91010,
Keywords: classification ; microarray gene expression ; shrinkage estimator
Abstract:

Many classification methods have been developed to predict tumor class based on gene expression microarray data. For such large p (number of genes) small n (sample size/number of chips) high-throughput data, feature selection, or dimension reduction, is an essential step. We propose a novel classification method based on some shrinkage estimator and fused it with the nearest centroid classifier. The implementation of the proposed procedure is very straightforward. Some variants of the proposed procedure are also investigated. We apply the new classification procedure to benchmark gene expression microarray data sets and compare the proposed method with several popular classification methods.


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