Enabling more sophisticated gene expression analysis for understanding diseases and optimizing treatments

Abstract

We survey the progress in the analysis of gene expression data for the purposes of disease subtype diagnosis, new subtype discovery, and understanding of diseases and treatment responses. We find existing works fall short on several issues: these works provide little information on the interplay between selected genes; the collection of pathways that can be used, evaluated, and ranked against the observed expression data is limited; and a comprehensive set of rules for reasoning about relevant molecular events has not been compiled and formalized. We thus envision an advanced integrated framework, and are developing a system based on it, to provide biologically inspired solutions. It comprises: (i) automated analysis and extraction of information from biomedical texts; (ii) targeted construction of known pathways; and (iii) direct hypothesis generation based on logical reasoning on, and tests for, consistencies and inconsistencies of observed data against known pathways.