Inference of boolean rules of gene sets for disease classification

Abstract

With the development of advanced genome-wide gene expression profiling technologies, it has become possible to simultaneously measure expression levels of thousands of genes in a population of cells under a specific condition, and now a large volume of gene expression data under the various experimental conditions is being rapidly accumulated in public repositories such as GEO (Gene Expression Omnibus) at the NCBI (National Center for Biotechnology Information) and ArrayExpress at the EBI (European Bioinformatics Institute), serving as invaluable resources for molecular cancer research. In a typical analysis of cancer gene expression profiles, individual genes are ranked by their statistical significance of the differential expression between two different experimental conditions (such as normal vs. tumor tissues); several tens of top-ranked genes are then selected for further analysis, such as cancer classification and functional enrichment analysis. One limitation of these individual gene ranking based approaches is that they are prone to produce unstable gene-lists which affect the results of subsequent analysis. Recently developed gene set analysis approaches aim to directly evaluate the statistical significance of differential expression patterns of groups of functionally relevant genes without a gene pre-selection step. These approaches have been considered to offer advantages over conventional functional enrichment analysis, followed by an individual gene ranking step, in detecting biological processes that show $\It{`subtle but coordinated expression changes`}$ between two different conditions. Moreover, a number of studies that use inferred gene set activity profiles for the purposes of various pattern analyses such as cancer classification and cancer subtype identification have shown bettern performance than individual gene based pattern analyses. To gain further insights about the molecular mechanisms of tumor developments, there have been a nu...