Scoring Genes for Relevance
Recent molecular level studies that compare different classes of disease conditions produce labeled gene expression data. We examine scoring methods that are useful in mining such gene expression data for genes that have biological relevance to the condition studied. Relevance information is useful in identifying genes driving the biological process, in selecting small subsets of genes with diagnostic potential, and in better understanding the condition studied and its relationship to known or hypothesized biochemical pathways. We present the scoring methods; describe a process for computing the corresponding p-values; and finally, present results from application to actual cancer gene expression data. These include applying classification techniques employing varying relevance based selected sets of genes.