TY - JOUR
T1 - Inferring data-specific micro-RNA function through the joint ranking of micro-RNA and pathways from matched micro-RNA and gene expression data
AU - Patrick, Ellis
AU - Buckley, Michael
AU - Müller, Samuel
AU - Lin, David M.
AU - Yang, Jean Y. H.
PY - 2015/9/1
Y1 - 2015/9/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84940747337&partnerID=8YFLogxK
UR - http://purl.org/au-research/grants/arc/FT0991918
UR - http://purl.org/au-research/grants/arc/DP130100488
U2 - 10.1093/bioinformatics/btv220
DO - 10.1093/bioinformatics/btv220
M3 - Article
C2 - 25910695
AN - SCOPUS:84940747337
SN - 1367-4803
VL - 31
SP - 2822
EP - 2828
JO - Bioinformatics
JF - Bioinformatics
IS - 17
ER -