000201866 001__ 201866
000201866 005__ 20190117190716.0
000201866 0247_ $$2doi$$a10.1093/bioinformatics/bti1116
000201866 022__ $$a1367-4803
000201866 037__ $$aARTICLE
000201866 245__ $$aPrediction of regulatory modules comprising microRNAs and target genes
000201866 269__ $$a2005
000201866 260__ $$bOxford University Press$$c2005
000201866 336__ $$aJournal Articles
000201866 520__ $$a<strong>Motivation:</strong> MicroRNAs (miRNAs) are small endogenous RNAs that can play important regulatory roles via the RNA-interference pathway by targeting mRNAs for cleavage or translational repression. We propose a computational method to predict miRNA regulatory modules (MRMs) or groups of miRNAs and target genes that are believed to participate cooperatively in post-transcriptional gene regulation. <strong>Results:</strong> We tested our method with the human genes and miRNAs, predicting 431 MRMs. We analyze a module with genes: BTG2, WT1, PPM1D, PAK7 and RAB9B, and miRNAs: miR-15a and miR-16. Review of the literature and annotation with Gene Ontology terms reveal that the roles of these genes can indeed be closely related in specific biological processes, such as gene regulation involved in breast, renal and prostate cancers. Furthermore, it has been reported that miR-15a and miR-16 are deleted together in certain types of cancer, suggesting a possible connection between these miRNAs and cancers. Given that most known functionalities of miRNAs are related to negative gene regulation, extending our approach and exploiting the insight thus obtained may provide clues to achieving practical accuracy in the reverse-engineering of gene regulatory networks.
000201866 700__ $$aYoon, Sungroh
000201866 700__ $$0240269$$aDe Micheli, Giovanni$$g167918
000201866 773__ $$j21$$ksuppl 2$$qii93-ii100$$tBioinformatics -Oxford-
000201866 8564_ $$s634772$$uhttps://infoscience.epfl.ch/record/201866/files/Bioinformatics-2005-Yoon-ii93-ii100.pdf$$yn/a$$zn/a
000201866 909C0 $$0252283$$pLSI1$$xU11140
000201866 909CO $$ooai:infoscience.tind.io:201866$$pSTI$$pIC$$particle
000201866 917Z8 $$x112915
000201866 937__ $$aEPFL-ARTICLE-201866
000201866 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000201866 980__ $$aARTICLE