Induction and selection of the most interesting Gene Ontology based multiattribute rules for descriptions of gene groups

A rules induction algorithm dedicated to describe groups of genes with similar expression profiles by means of Gene Ontology terms is discussed in the paper. The presented algorithm takes into consideration information contained in the Gene Ontology graph. A huge number of created rules requires defining the rules quality and similarity measures, thus the paper presents such measures and proposes a new method of the most interesting rules selection. Features reduction method based on the rough sets theory is adopted and applied in order to reduce the number of Gene Ontology terms occurring in rules. The paper presents results of performed experiments and describes shortly the internet application RuleGO in which the proposed methods were implemented.

Research highlights
► Rough sets based algorithm for GO terms reduction using attributes values preference.
► Improved gene groups description obtained using rules induction and GO terms hierarchy.
► New compound (objective–subjective) interestingness measure for GO terms based rules.
► Rules filtration substantially reduces number of rules needed for genes description.