The detection of composite miRNA functional module (CMFM) is of tremendous\nsignificance and helps in understanding the organization, regulation and execution of cell processes\nin cancer, but how to identify functional CMFMs is still a computational challenge. In this paper we\npropose a novel module detection method called MBCFM (detecting Composite Function Modules\nbased on Maximal Biclique enumeration), specifically designed to bicluster miRNAs and target\nmessenger RNAs (mRNAs) on the basis of multiple biological interaction information and topical\nnetwork features. In this method, we employ algorithm MICA to enumerate all maximal bicliques\nand further extract R-pairs from the miRNA-mRNA regulatory network. Compared with two\nexisting methods, Mirsynergy and SNMNMF on ovarian cancer dataset, the proposed method of\nMBCFM is not only able to extract cohesiveness-preserved CMFMs but also has high efficiency in\nrunning time. More importantly, MBCFM can be applied to detect other cancer-associated miRNA\nfunctional modules.
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