Gene expression mining for cohesive pattern discovery

Abstract

Identification of genes that share common biological functions is one of the main objectives in molecular biology. The present work focuses on developing an appropriate mechanism for discovery of such genesets from microarray data. We introduce a conceptual property 'cohesion' among genes as representative of common biological function, under influence of which a geneset behave coherently. Such genesets are marked as 'cohesive'. Depending on '100% cohesion' equivalence relation, the entire set of associated genes is decomposed into a number of disjoint equivalence classes, each with unique behavior. The equivalence classes form several disjoint affinity groups, members within a group having pair-wise direct interaction. Each group may be called a cohesive gene cluster. A data mining technique for cohesive geneset discovery is developed and applied on expression data to discover intracluster gene relationships for extracting natural coherent genesets. Experiments with some cancer datasets discover thousands of long confident patterns within reasonable time, showing its superiority over classical association discovery techniques. Results can provide important insight into molecular biology and biomedical research. © Springer-Verlag Berlin Heidelberg 2008.