Feature Selection and Analysis of Gene Expression Data Using Low-Dimensional Linear Programming
Satish Ch. Panigrahi; Md. Shafiul Alam alam9@uwindsor.ca; Asish Mukhopadhyay University of Windsor, Windsor, Ontario, Canada
Abstract
The availability of large volumes of gene expression data from microarray analysis [complementary DNA (cDNA) and oligonucleotide] has opened the door to the diagnoses and treatments of various diseases based on gene expression profiling. This chapter discusses a new profiling tool based on linear programming. Given gene expression data from two subclasses of the same disease (e.g., leukemia), we were able to determine efficiently if the samples are LS with respect to triplets of genes. This was left ...
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