Mirsynergy

Identification of microRNA regulatory modules (MiRMs) will aid deciphering aberrant transcriptional regulatory network in cancer but is computationally challenging. Existing methods are stochastic or require a fixed number of regulatory modules.
We propose Mirsynergy, an efficient deterministic overlapping clustering algorithm adapted from a recently developed framework. Mirsynergy operates in two stages: it first forms MiRMs based on co-occurring microRNA (miRNA) targets and then expands each MiRM by greedily including (excluding) mRNAs into (from) the MiRM to maximize the synergy score, which is a function of miRNA-mRNA and gene-gene interactions. Using expression data for ovarian, breast and thyroid cancer from The Cancer Genome Atlas, we compared Mirsynergy with internal controls and existing methods. Mirsynergy-MiRMs exhibit significantly higher functional enrichment and more coherent miRNA-mRNA expression anti-correlation. Based on Kaplan-Meier survival analysis, we proposed several prognostically promising MiRMs and envisioned their utility in cancer research.
Mirsynergy is implemented/available as an R/Bioconductor package at www.cs.utoronto.ca/~yueli/Mirsynergy.html.[1]

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