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dc.contributor.authorAn, Jay
dc.contributor.authorChoi, Kwok Pui
dc.contributor.authorWells, Christine
dc.contributor.authorChen, Yi-Ping Phoebe
dc.date.accessioned2017-05-03T11:47:05Z
dc.date.available2017-05-03T11:47:05Z
dc.date.issued2010
dc.date.modified2011-03-07T08:56:25Z
dc.identifier.issn02197200
dc.identifier.doi10.1142/S0219720010004574
dc.identifier.urihttp://hdl.handle.net/10072/36954
dc.description.abstractCurrent miRNA target prediction tools have the common problem that their false positive rate is high. This renders identification of co-regulating groups of miRNAs and target genes unreliable. In this study, we describe a procedure to identify highly probable co-regulating miRNAs and the corresponding co-regulated gene groups. Our procedure involves a sequence of statistical tests: (1) identify genes that are highly probable miRNA targets; (2) determine for each such gene, the minimum number of miRNAs that co-regulate it with high probability; (3) find, for each such gene, the combination of the determined minimum size of miRNAs that co-regulate it with the lowest p-value; and (4) discover for each such combination of miRNAs, the group of genes that are co-regulated by these miRNAs with the lowest p-value computed based on GO term annotations of the genes. Results: Our method identifies 4, 3 and 2-term miRNA groups that co-regulate gene groups of size at least 3 in human. Our result suggests some interesting hypothesis on the functional role of several miRNAs through a "guilt by association" reasoning. For example, miR-130, miR-19 and miR-101 are known neurodegenerative diseases associated miRNAs. Our 3-term miRNA table shows that miR-130/19/101 form a co-regulating group of rank 22 (p-value =1.16 נ10-2). Since miR-144 is co-regulating with miR-130, miR-19 and miR-101 of rank 4 (p-value = 1.16 נ10-2) in our 4-term miRNA table, this suggests hsa-miR-144 may be neurodegenerative diseases related miRNA. Conclusions: This work identifies highly probable co-regulating miRNAs, which are refined from the prediction by computational tools using (1) signal-to-noise ratio to get high accurate regulating miRNAs for every gene, and (2) Gene Ontology to obtain functional related co-regulating miRNA groups. Our result has partly been supported by biological experiments. Based on prediction by TargetScanS, we found highly probable target gene groups in the Supplementary Information. This result might help biologists to find small set of miRNAs for genes of interest rather than huge amount of miRNA set. Supplementary Information: http://www.deakin.edu.au/~phoebe/JBCBAnChen/JBCB.htm.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherImperial College Press
dc.publisher.placeUnited Kingdom
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom99
dc.relation.ispartofpageto115
dc.relation.ispartofissue1
dc.relation.ispartofjournalJournal of Bioinformatics and Computational Biology
dc.relation.ispartofvolume8
dc.rights.retentionY
dc.subject.fieldofresearchBiochemistry and cell biology
dc.subject.fieldofresearchBiochemistry and cell biology not elsewhere classified
dc.subject.fieldofresearchcode3101
dc.subject.fieldofresearchcode310199
dc.titleIdentifying co-regulating microRNA groups
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, Griffith Institute for Drug Discovery
gro.date.issued2010
gro.hasfulltextNo Full Text
gro.griffith.authorWells, Christine
gro.griffith.authorAn, Jay


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