title: Inferring data-specific micro-RNA function through the joint ranking of micro-RNA and pathways from matched micro-RNA and gene expression data.
abstract: BACKGROUND
  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.
  
  
  METHODS
  This framework is implemented as an R function, pMim, in the package sydSeq available from http://www.ellispatrick.com/r-packages.
  
  
  BACKGROUND
  jean.yang@sydney.edu.au
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia.
authors.forename: Ellis
authors.initials: E
authors.lastname: Patrick
authors.affiliation: CSIRO Mathematical & Information Sciences, Clayton South, VIC 3168, Australia and.
authors.forename: Michael
authors.initials: M
authors.lastname: Buckley
authors.affiliation: School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia.
authors.forename: Samuel
authors.initials: S
authors.lastname: Müller
authors.affiliation: Department of Biomedical Sciences, Cornell University, Ithaca, NY, USA.
authors.forename: David M
authors.initials: DM
authors.lastname: Lin
authors.affiliation: School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia.
authors.forename: Jean Y H
authors.initials: JY
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D020869:Gene Expression Profiling / Q000379:methods; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D006801:Humans; D035683:MicroRNAs / Q000235:genetics / Q000378:metabolism*; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism*; D012984:Software
chemical_list: D035683:MicroRNAs; D012333:RNA, Messenger