A Local Genetic Algorithm for the Identification of Condition-Specific MicroRNA-Gene Modules

Transcription factor and microRNA are two types of key regulators of gene expression. Their regulatory mechanisms are highly complex. In this study, we propose a computational method to predict condition-speci􀄕c regulatory modules that consist of microRNAs, transcription factors, and their commonly regulated genes. We used matched global expression pro􀄕les of mRNAs and microRNAs together with the predicted targets of transcription factors and microRNAs to construct an underlying regulatory network. Our method searches for highly scored modules from the network based on a two-step heuristic method that combines genetic and local search algorithms. Using two matched expression datasets, we demonstrate that our method can identify highly scored modules with statistical signi􀄕cance and biological relevance. e identi􀄕ed regulatory modules may provide useful insights on the mechanisms of transcription factors and microRNAs.