Abstract #300324

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JSM 2003 Abstract #300324
Activity Number: 471
Type: Contributed
Date/Time: Thursday, August 7, 2003 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #300324
Title: Greedy Learning from Multiple Gene Profiles for Selecting Informative Genes in Molecular Cancer Classification
Author(s): Xin Yan*+ and Shaw-Hwa Lo
Companies: Columbia University and Columbia University
Address: 542 West 112th St. #2B, New York, NY, 10025-1648,
Keywords: gene selection ; cancer classification ; association score ; multiple gene profiles
Abstract:

A lot of marginal statistical methods have been applied to gene expression data despite the fact that the effects of gene interactions are non-negligible. Considering a typical two-category cancer classification problem, we proposed a new, multi-gene method to evaluate the importance of the "Informative Genes" we are looking for. A gene profile association score (GPAS) was defined based on a k-gene subset (e.g., when k=10, a random combination of 10 genes out of the total) .By greedy learning from sufficiently large number of these profiles, "Informative Genes" were pulled out by their exceptionally high return frequencies. Agreed with the results derived by marginal gene selection methods, this multi-gene method also picked up some comparatively large p valued genes, which may be favorable to biological explanations.

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