title: Identification of miRNA-mRNA regulatory modules by exploring collective group relationships.
abstract: BACKGROUND
  microRNAs (miRNAs) play an essential role in the post-transcriptional gene regulation in plants and animals. They regulate a wide range of biological processes by targeting messenger RNAs (mRNAs). Evidence suggests that miRNAs and mRNAs interact collectively in gene regulatory networks. The collective relationships between groups of miRNAs and groups of mRNAs may be more readily interpreted than those between individual miRNAs and mRNAs, and thus are useful for gaining insight into gene regulation and cell functions. Several computational approaches have been developed to discover miRNA-mRNA regulatory modules (MMRMs) with a common aim to elucidate miRNA-mRNA regulatory relationships. However, most existing methods do not consider the collective relationships between a group of miRNAs and the group of targeted mRNAs in the process of discovering MMRMs. Our aim is to develop a framework to discover MMRMs and reveal miRNA-mRNA regulatory relationships from the heterogeneous expression data based on the collective relationships.
  
  
  RESULTS
  We propose DIscovering COllective group RElationships (DICORE), an effective computational framework for revealing miRNA-mRNA regulatory relationships. We utilize the notation of collective group relationships to build the computational framework. The method computes the collaboration scores of the miRNAs and mRNAs on the basis of their interactions with mRNAs and miRNAs, respectively. Then it determines the groups of miRNAs and groups of mRNAs separately based on their respective collaboration scores. Next, it calculates the strength of the collective relationship between each pair of miRNA group and mRNA group using canonical correlation analysis, and the group pairs with significant canonical correlations are considered as the MMRMs. We applied this method to three gene expression datasets, and validated the computational discoveries.
  
  
  CONCLUSIONS
  Analysis of the results demonstrates that a large portion of the regulatory relationships discovered by DICORE is consistent with the experimentally confirmed databases. Furthermore, it is observed that the top mRNAs that are regulated by the miRNAs in the identified MMRMs are highly relevant to the biological conditions of the given datasets. It is also shown that the MMRMs identified by DICORE are more biologically significant and functionally enriched.
authors.affiliation: School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, Adelaide, 5095, SA, Australia. masud.karim@mymail.unisa.edu.au.
authors.forename: S M
authors.initials: SM
authors.lastname: Masud Karim
authors.affiliation: School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, Adelaide, 5095, SA, Australia. Lin.Liu@unisa.edu.au.
authors.forename: Lin
authors.initials: L
authors.lastname: Liu
authors.affiliation: School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, Adelaide, 5095, SA, Australia. Thuc.Le@unisa.edu.au.
authors.forename: Thuc Duy
authors.initials: TD
authors.lastname: Le
authors.affiliation: School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, Adelaide, 5095, SA, Australia. Jiuyong.Li@unisa.edu.au.
authors.forename: Jiuyong
authors.initials: J
authors.lastname: Li
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology; D030541:Databases, Genetic; D053263:Gene Regulatory Networks; D006801:Humans; D035683:MicroRNAs / Q000378:metabolism*; D009369:Neoplasms / Q000235:genetics / Q000473:pathology; D012333:RNA, Messenger / Q000378:metabolism*; D059467:Transcriptome
chemical_list: D035683:MicroRNAs; D012333:RNA, Messenger
references.citation: Curr Genomics. 2010 Apr;11(2):129-35
references.pmid: 20885820
references.citation: Nucleic Acids Res. 2000 Jan 1;28(1):27-30
references.pmid: 10592173
references.citation: J Biomed Inform. 2009 Aug;42(4):685-91
references.pmid: 19535005
references.citation: BMC Bioinformatics. 2008 Dec 12;9 Suppl 12:S5
references.pmid: 19091028
references.citation: Nucleic Acids Res. 2009 Jan;37(Database issue):D105-10
references.pmid: 18996891
references.citation: Nucleic Acids Res. 2011 Feb;39(3):825-36
references.pmid: 20929877
references.citation: Cell. 2005 Jan 14;120(1):15-20
references.pmid: 15652477
references.citation: Nucleic Acids Res. 2000 Jan 1;28(1):316-9
references.pmid: 10592259
references.citation: Nature. 2004 Nov 11;432(7014):226-30
references.pmid: 15538371
references.citation: Nat Methods. 2012 Mar 18;9(5):471-2
references.pmid: 22426491
references.citation: EMBO J. 2014 Sep 17;33(18):2040-56
references.pmid: 25069772
references.citation: PLoS One. 2015 Jun 26;10(6):e0131627
references.pmid: 26114448
references.citation: Nature. 2004 Sep 16;431(7006):350-5
references.pmid: 15372042
references.citation: Nature. 2005 Jun 9;435(7043):834-8
references.pmid: 15944708
references.citation: Science. 2011 Dec 16;334(6062):1518-24
references.pmid: 22174245
references.citation: Bioinformatics. 2011 Jul 1;27(13):i401-9
references.pmid: 21685098
references.citation: Nucleic Acids Res. 2014 Jan;42(Database issue):D78-85
references.pmid: 24304892
references.citation: Bioinformatics. 2014 Sep 15;30(18):2627-35
references.pmid: 24894504
references.citation: Bioinformatics. 2013 Jul 1;29(13):i89-97
references.pmid: 23813013
references.citation: Int J Cancer. 2014 Feb 1;134(3):542-51
references.pmid: 23904094
references.citation: Cell. 2009 Feb 20;136(4):586-91
references.pmid: 19239879
references.citation: Nucleic Acids Res. 2015 Jan;43(Database issue):D153-9
references.pmid: 25416803
references.citation: Bioinformatics. 2005 Sep 1;21 Suppl 2:ii93-100
references.pmid: 16204133
references.citation: Genome Res. 2009 Jan;19(1):92-105
references.pmid: 18955434
references.citation: J Cell Sci. 2007 Sep 1;120(Pt 17):3045-52
references.pmid: 17715156
references.citation: Nat Neurosci. 2004 Feb;7(2):113-7
references.pmid: 14703574
references.citation: Nat Rev Cancer. 2006 Apr;6(4):259-69
references.pmid: 16557279
references.citation: Nature. 2008 Oct 23;455(7216):1061-8
references.pmid: 18772890
references.citation: BMC Genomics. 2009 Aug 11;10:373
references.pmid: 19671175
references.citation: Cell. 2004 Jan 23;116(2):281-97
references.pmid: 14744438
references.citation: Nat Rev Genet. 2008 Nov;9(11):831-42
references.pmid: 18852696
references.citation: Mol Syst Biol. 2006;2:46
references.pmid: 16969338
references.citation: Bioinformatics. 2009 Feb 1;25(3):387-93
references.pmid: 19056778
references.citation: Comput Biol Med. 2012 Apr;42(4):428-37
references.pmid: 22245099
references.citation: Nature. 2005 Jul 14;436(7048):214-20
references.pmid: 15951802
references.citation: Nat Rev Cancer. 2006 Nov;6(11):857-66
references.pmid: 17060945
references.citation: Nat Genet. 2006 Feb;38(2):228-33
references.pmid: 16380711
references.citation: Bioinformatics. 2009 Apr 15;25(8):1063-9
references.pmid: 19131366
references.citation: Cell. 2009 Jan 23;136(2):215-33
references.pmid: 19167326
references.citation: Genome Med. 2011 Aug 31;3(8):56
references.pmid: 21888691
references.citation: Mutat Res. 2011 Jun 17;722(2):94-105
references.pmid: 20472093
references.citation: Bioinformatics. 2010 Dec 15;26(24):3105-11
references.pmid: 20956247
references.citation: Nucleic Acids Res. 2012 Jul;40(Web Server issue):W478-83
references.pmid: 22573175
references.citation: Nucleic Acids Res. 2011 Jan;39(Database issue):D698-704
references.pmid: 21071413
references.citation: Bioinformatics. 2007 May 1;23(9):1141-7
references.pmid: 17350973
references.citation: Annu Rev Pathol. 2009;4:199-227
references.pmid: 18817506
references.citation: Cell. 2001 Dec 28;107(7):823-6
references.pmid: 11779458
references.citation: J Biomed Inform. 2011 Oct;44(5):839-47
references.pmid: 21605702