Inferring microRNA-mRNA causal regulatory relationships from expression data

Bioinformatics

Volumn 29, Issue 6, 2013, Pages 765-771

  Le, Thuc Duy ^a   Liu, Lin ^a   Tsykin, Anna ^b   Goodall, Gregory J ^b,c   Liu, Bing ^d   Sun, Bing Yu ^e   Li, Jiuyong ^a  

^a UNIVERSITY OF SOUTH AUSTRALIA   (Australia)

^b CENTRE FOR CANCER BIOLOGY   (Australia)

^c UNIVERSITY OF ADELAIDE   (Australia)

^d CHILDREN S CANCER INSTITUTE AUSTRALIA FOR MEDICAL RESEARCH   (Australia)

^e INSTITUTE OF INTELLIGENT MACHINES   (China)

Author keywords

[No Author keywords available]

Indexed keywords

MESSENGER RNA; MICRORNA;

ALGORITHM; ANIMAL; ARTICLE; EPITHELIAL MESENCHYMAL TRANSITION; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENETICS; METABOLISM; TUMOR CELL LINE;

ALGORITHMS; ANIMALS; CELL LINE, TUMOR; EPITHELIAL-MESENCHYMAL TRANSITION; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; MICRORNAS; RNA, MESSENGER;

EID: 84875145131     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btt048     Document Type: Article

References (37)

1. Ambros,V. (2004) The functions of animal microRNAs. Nature, 431, 350-355.
2. Bartel,D.P. (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell, 116, 281-297.
3. Bartel,D.P. (2009) MicroRNAs: target recognition and regulatory functions. Cell, 136, 215-233.
4. Berezikov,E. et al. (2006) Approaches to microRNA discovery. Nat. Genet., 38, 2-8.
5. Betel,D. et al. (2010) Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol., 11, R90.
6. Chen,J.F. et al. (2006) The role of microRNA-1 and microRNA-133 in skeletal muscle proliferation and differentiation. Nat. Genet., 38, 228-233.
7. Chickering,D.M. (2002) Learning equivalence classes of Bayesian-network structures. J. Mach. Learn. Res., 2, 445-498.
8. Chow,K. and Liu,N.C. (1968) Approximating discrete probability distributions with dependence trees. IEEE Trans. Inf. Theory, 14, 462-467.
9. Cui,Q. et al. (2006) Principles of microRNA regulation of a human cellular signaling network. Mol. Syst. Biol., 2, 1-7.
10. Esquela-Kerscher,A. and Slack,F.J. (2006) Oncomirs-microRNAs with a role in cancer. Nat. Rev. Cancer, 6, 259-269.
11. Gregory,P.A. et al. (2008) The miR-200 family and miR-205 regulate epithelial to mesenchymal transition by targeting ZEB1 and SIP1. Nat. Cell Biol., 10, 593-601.
12. Heckerman,D. and Chickering,D.M. (1995) Learning Bayesian networks: the combination of knowledge and statistical data metrics for belief networks. Mach. Learn., 20, 197-243.
13. Huang,J.C. et al. (2007) Using expression profiling data to identify human microRNA targets. Nat. Methods, 4, 1045-1050.
14. Jin,P. et al. (2004) Biochemical and genetic interaction between the fragile X mental retardation protein and the microRNA pathway. Nat. Neurosci., 7, 113-117.
15. Joung,J.G. and Fei,Z. (2009) Identification of microRNA regulatory modules in Arabidopsis via a probabilistic graphical model. Bioinformatics, 25, 387-393.
16. Joung,J.G. et al. (2007) Discovery of microRNA-mRNA modules via populationbased probabilistic learning. Bioinformatics, 23, 1141-1147.
17. Kalisch,M. and Buhlmann,P. (2007) Estimating high-dimensional directed acyclic graphs with the PC-algorithm. J. Mach. Learn. Res., 8, 613-636.
18. Kim,V.N. and Nam,J.W. (2006) Genomics of microRNA. Trends Genet., 22, 165-173.
19. Lauritzen,S. (1996) Graphical Models. Oxford University Press, Oxford.
20. Liu,B. et al. (2009) Discovery of functional miRNA mRNA regulatory modules with computational methods. J. Biomed. Inf, 42, 685-691.
21. Liu,B. et al. (2010) Identifying functional miRNA-mRNA regulatory modules with correspondence latent dirichlet allocation. Bioinformatics, 26, 3105-3111.
22. Maathuis,H.M. et al. (2009) Estimating high-dimensional intervention effects from observational data. Ann. Stat., 37, 3133-3164.
23. Maathuis,H.M. et al. (2010) Predicting causal effects in large-scale systems from observational data. Nat. Methods, 7, 247-249.
24. Meister,G. and Tuschl,T. (2004) Mechanisms of gene silencing by double-stranded RNA. Nature, 431, 343-349.
25. Neapolitan,R. (2004) Learning Bayesian Networks. Pearson Prenctice Hall, Upper Saddle River, NJ.
26. Pearl,J. (2000) Causality: Models, Reasoning, and Inference. Cambridge University Press, Cambridge.
27. Poy,M.N. et al. (2004) A pancreatic islet-specific microRNA regulates insulin secretion. Nature, 432, 226-230.
28. Robinson,J. (1971) Counting labeled acyclic digraphs. In: New Directions in the Theory of Graphs: Proceedings of the Third Ann Arbor Conference on Graph Theory. Academic Press, New York, pp. 239-273.
29. Smyth,G.K. (2005) Limma: linear models for microarray data. In: Gentleman,R. et al. (eds) Bioinformatics and Computational Biology Solutions using R and Bioconductor. pp. 397-420.
30. Søkilde,R. et al. (2011) Global microRNA analysis of the NCI-60 cancer cell panel. Mol. Cancer Ther., 10, 375-384.
31. Spirtes,P. et al. (2000) Causation, Prediction, and Search, 2nd edn. MIT Press, Cambridge, MA.
32. Tran,D.H. et al. (2008) Finding microRNA regulatory modules in human genome using rule induction. BMC Bioinformatics, 9 (Suppl. 12), S5.
33. Verma,T. and Pearl,J. (1990) Equivalence and synthesis of causal models. In: Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, San Francisco, CA, pp. 220-227.
34. Xu,C. et al. (2007) The muscle-specific microRNAs miR-1 and miR-133 produce opposing effects on apoptosis by targeting HSP60, HSP70 and caspase-9 in cardiomyocytes. J. Cell Sci., 120 (Pt. 17), 3045-3052.
35. Zeisberg,M. and Neilson,E.G. (2009) Biomarkers for epithelial-mesenchymal transitions. J. Clin. Invest., 119, 1429-1437.
36. Zhao,Y. et al. (2005) Serum response factor regulates a muscle-specific microRNA that targets Hand2 during cardiogenesis. Nature, 436, 214-220.
37. Zisoulis,D.G. et al. (2012) Autoregulation of microRNA biogenesis by let-7 and Argonaute. Nature, 486, 541-544.