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- Title
Inferring data-specific micro-RNA function through the joint ranking of micro-RNA and pathways from matched micro-RNA and gene expression data.
- Authors
Patrick, Ellis; Buckley, Michael; Müller, Samuel; Lin, David M.; Yang, Jean Y. H.
- Abstract
Motivation: In practice, identifying and interpreting the functional impacts of the regulatory relationships between micro-RNA and messenger-RNA is non-trivial. The sheer scale of possible micro-RNA and messenger-RNA interactions can make the interpretation of results difficult. Results: We propose a supervised framework, pMim, built upon concepts of significance combination, for jointly ranking regulatory micro-RNA and their potential functional impacts with respect to a condition of interest. Here, pMim directly tests if a micro-RNA is differentially expressed and if its predicted targets, which lie in a common biological pathway, have changed in the opposite direction. We leverage the information within existing micro-RNA target and pathway databases to stabilize the estimation and annotation of micro-RNA regulation making our approach suitable for datasets with small sample sizes. In addition to outputting meaningful and interpretable results, we demonstrate in a variety of datasets that the micro-RNA identified by pMim, in comparison to simpler existing approaches, are also more concordant with what is described in the literature
- Subjects
MICRORNA; GENE expression; GENE regulatory networks; SAMPLE size (Statistics); RANKING (Statistics)
- Publication
Bioinformatics, 2015, Vol 31, Issue 17, p2822
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btv220