Fig. 1

DICORE workflow. Given the inputs of miRNA and mRNA expression profiles, we first derive an expression-based interaction weights matrix W using correlation test. We then compute two collaboration score matrices S and T from W for miRNAs and mRNAs based on their functional interaction similarities with common mRNAs (or miRNAs), respectively. Using these collaboration scores as input, we separately generate groups of miRNAs and groups of mRNAs at Stage 1 by an overlapping neighborhood expansion clustering algorithm, in which miRNAs or mRNAs are greedily added to (removed from) each cluster of miRNAs or mRNAs, respectively that maximize cohesiveness score of the cluster. Next in Stage 2, we apply canonical correlation analysis on the groups of miRNAs and groups of mRNAs to obtain significant collective group relationships, which are eventually the MMRMs with strength scores