title: Inferring microRNA and transcription factor regulatory networks in heterogeneous data.
abstract: BACKGROUND
  Transcription factors (TFs) and microRNAs (miRNAs) are primary metazoan gene regulators. Regulatory mechanisms of the two main regulators are of great interest to biologists and may provide insights into the causes of diseases. However, the interplay between miRNAs and TFs in a regulatory network still remains unearthed. Currently, it is very difficult to study the regulatory mechanisms that involve both miRNAs and TFs in a biological lab. Even at data level, a network involving miRNAs, TFs and genes will be too complicated to achieve. Previous research has been mostly directed at inferring either miRNA or TF regulatory networks from data. However, networks involving a single type of regulator may not fully reveal the complex gene regulatory mechanisms, for instance, the way in which a TF indirectly regulates a gene via a miRNA.
  
  
  RESULTS
  We propose a framework to learn from heterogeneous data the three-component regulatory networks, with the presence of miRNAs, TFs, and mRNAs. This method firstly utilises Bayesian network structure learning to construct a regulatory network from multiple sources of data: gene expression profiles of miRNAs, TFs and mRNAs, target information based on sequence data, and sample categories. Then, in order to produce more meaningful results for further biological experimentation and research, the method searches the learnt network to identify the interplay between miRNAs and TFs and applies a network motif finding algorithm to further infer the network.We apply the proposed framework to the data sets of epithelial-to-mesenchymal transition (EMT). The results elucidate the complex gene regulatory mechanism for EMT which involves both TFs and miRNAs. Several discovered interactions and molecular functions have been confirmed by literature. In addition, many other discovered interactions and bio-markers are of high statistical significance and thus can be good candidates for validation by experiments. Moreover, the results generated by our method are compact, involving a small number of interactions which have been proved highly relevant to EMT.
  
  
  CONCLUSIONS
  We have designed a framework to infer gene regulatory networks involving both TFs and miRNAs from multiple sources of data, including gene expression data, target information, and sample categories. Results on the EMT data sets have shown that the proposed approach is able to produce compact and meaningful gene regulatory networks that are highly relevant to the biological conditions of the data sets. This framework has the potential for application to other heterogeneous datasets to reveal the complex gene regulatory relationships.
authors.affiliation: School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA 5095, Australia. leyty017@mymail.unisa.edu.au
authors.forename: Thuc D
authors.initials: TD
authors.lastname: Le
authors.forename: Lin
authors.initials: L
authors.lastname: Liu
authors.forename: Bing
authors.initials: B
authors.lastname: Liu
authors.forename: Anna
authors.initials: A
authors.lastname: Tsykin
authors.forename: Gregory J
authors.initials: GJ
authors.lastname: Goodall
authors.forename: Kenji
authors.initials: K
authors.lastname: Satou
authors.forename: Jiuyong
authors.initials: J
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002460:Cell Line; D058750:Epithelial-Mesenchymal Transition / Q000235:genetics; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans; D035683:MicroRNAs / Q000378:metabolism*; D012333:RNA, Messenger / Q000378:metabolism; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D035683:MicroRNAs; D012333:RNA, Messenger; D014157:Transcription Factors