MicroRNAs play an important role in regulation of gene expression, but still detection of their targets remains a challenge. In this work we present a supervised regulatory network inference method with aim to identify potential target genes (mRNAs) of microRNAs. Briefly, the proposed method exploiting mRNA and microRNA expression trains Random Forests on known interactions and subsequently it is able to predict novel ones. In parallel, we incorporate different available data sources, such as Gene Ontology and ProteinProtein Interactions, to deliver biologically consistent results. Application in both benchmark data and an experiment studying aging showed robust performance.
|Original language||English (US)|
|Number of pages||4|
|Journal||Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference|
|State||Published - 2014|
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