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Summary: Exploring Classifiability Metrics for Selecting Informative
Genes
James Wua
, and Ioannis P. Androulakis a,b,
a
Chemical & Biochemical Engineering Department and
b
Biomedical Engineering Department
Rutgers, The State University of New Jersey
Piscataway, NJ 08854
Abstract
Microarray experiments are emerging as one of the main driving forces in modern
biology. By allowing the simultaneous monitoring of the expression of the entire
genome for a given organism, array experiments provide tremendous insight into the
fundamental biological processes that translate genetic information. One of the major
challenges is to identify computationally efficient and biologically meaningful analysis
approaches to extract the most informative and unbiased components of the microarray
data. In this paper we introduce a framework that integrates machine learning and
optimization techniques for the selection of maximally informative genes in microarray
expression experiments. The proposed approach provides tremendous reduction in the
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