| Popis: |
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 as an open problem in an earlier study, which considered only pairs of genes as linear separators. Our tool comes in two versions—offline and incremental. Tests show that the incremental version is markedly more efficient than the offline one. This chapter also introduces a gene selection strategy that exploits the class distinction property of a gene by a separability test using pairs and triplets. We applied our gene selection strategy to four publicly available gene-expression data sets. Our experiments show that gene spaces generated by our method achieves similar or even better classification accuracy than the gene spaces generated by t -values, Fisher criterion score (FCS), and significance analysis of microarrays (SAM). |
