Learning Relational Descriptions of Differentially Expressed Gene Groups

Abstract-- This paper presents a method that uses gene ontologies, together with the paradigm of relational subgroup discovery, to find compactly described groups of genes differentially expressed in specific cancers. The groups are described by means of relational logic features, extracted from publicly available gene ontology information, and are straightforwardly interpretable by medical experts. We applied the proposed method to three gene expression data sets with the following respective sets of sample classes: (i) acute lymphoblastic leukemia (ALL) vs. acute myeloid leukemia (AML), (ii) seven subtypes of ALL, and (iii) fourteen different types of cancers. Significant number of discovered groups of genes had a description which highlighted the underlying biological process that is responsible for distinguishing one class from the other classes. The quality of the discovered descriptions was also verified by crossvalidation. We believe that the presented approach will significantl...

• Type: Journal
• Year: 2008
• Where: TSMC
• Authors: Igor Trajkovski, Filip Zelezný, Nada Lavrac, Jakub Tolar
• Keywords: Gene, Gene Expression Data, Relational Subgroup Discovery, TSMC 2008