JSM 2004 - Toronto

Abstract #301550

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Activity Number: 384
Type: Contributed
Date/Time: Wednesday, August 11, 2004 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #301550
Title: A Nonparametric Measure to Detect Interactive Information in Discriminant Analysis of Gene Expression
Author(s): Xin Yan*+ and Tian Zheng and Shaw-Hwa Lo
Companies: Columbia University and Columbia University and Columbia University
Address: 542 West 112th St. #2B, New York, NY, 10025,
Keywords: Gene Profile Association Score ; gene expression ; breast cancer ; informative gene
Abstract:

Despite biological acknowledge of influence of gene interactions, marginal statistical measures were broadly used on feature selection problems in gene expression data. In our previous paper, we proposed using Gene Profile Association Score (GPAS), a statistical measure to explore higher-order interactive gene information with respect to binary categorical difference. Original GPAS however, was somewhat arguable because of its assumed input of discritized data, thus an potential loss of information. Inspired by the analysis of spatial marked point process, we now introduce our sign-test based Gene Profile Association Score (sGPAS), where all numerical values of expression will be taken into account. Similar to what we did previously, the performance of sGPAS was compared to a variety of predictors. Empirical validation results showed substantial improvement to all marginal predictors and a comparable performance to the original GPAS predictor. SGPAS provides an alternative to GPAS, allowing people to extract differentiation information with continuous expression results, while preserving the gene-to-gene interactions at the same time.

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