title: Predicting the target genes of intronic microRNAs using large-scale gene expression data.
abstract: Current microRNA target prediction techniques provide long lists of putative miRNA-target interactions, many of which are false positives. The goal of this paper is to identify functional targets in these lists based on biological evidence obtained from the expression profiles of the host genes of intronic miRNAs and those of their targets. We propose a scoring strategy for each interaction based on the combinatorial effect of miRNAs. In particular, the change in expression level of a target gene is expressed in terms of a linear combination of the host gene data which are used as surrogates for expression data of the intronic miRNAs. The parameters of this linear model give an estimate of the contribution of each intronic miRNA in down-regulating the target gene. The experimental results show that our prediction technique is able to detect several functional interactions. In addition, the analysis of mRNA microarrays after intronic miRNA transfection confirms that significantly down-regulated genes are among targets detected by our technique.
authors.affiliation: Department of Electrical and Computer Engineering and with Institute of Biomaterial and Biomedical Engineering, at University of Toronto, Canada. h.radfar@utoronto.ca
authors.forename: M
authors.initials: M
authors.lastname: Radfar
authors.forename: Willy
authors.initials: W
authors.lastname: Wong
authors.forename: Quaid D
authors.initials: QD
authors.lastname: Morris
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D035683:MicroRNAs / Q000235:genetics*; D008957:Models, Genetic; D011485:Protein Binding; D011506:Proteins / Q000235:genetics*; D012333:RNA, Messenger / Q000235:genetics*
chemical_list: D035683:MicroRNAs; D011506:Proteins; D012333:RNA, Messenger