Copyright © 2013 Mario Flores et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

1. M. Schena, D. Shalon, R. W. Davis, and P. O. Brown, “Quantitative monitoring of gene expression patterns with a complementary DNA microarray,” Science, vol. 270, no. 5235, pp. 467–470, 1995. View at Scopus
2. E. R. Mardis, “Next-generation DNA sequencing methods,” Annual Review of Genomics and Human Genetics, vol. 9, pp. 387–402, 2008. View at Publisher · View at Google Scholar · View at Scopus
3. Cancer Genome Atlas Network, “Comprehensive molecular portraits of human breast tumours,” Nature, vol. 490, pp. 61–70, 2012.
4. D. Bell, A. Berchuck, M. Birrer et al., “Integrated genomic analyses of ovarian carcinoma,” Nature, vol. 474, no. 7353, pp. 609–615, 2011. View at Publisher · View at Google Scholar · View at Scopus
5. R. McLendon, A. Friedman, D. Bigner et al., “Comprehensive genomic characterization defines human glioblastoma genes and core pathways,” Nature, vol. 455, no. 7216, pp. 1061–1068, 2008. View at Publisher · View at Google Scholar · View at Scopus
6. C. M. Perou, T. Sørile, M. B. Eisen et al., “Molecular portraits of human breast tumours,” Nature, vol. 406, no. 6797, pp. 747–752, 2000. View at Publisher · View at Google Scholar
7. J. Lapointe, C. Li, J. P. Higgins et al., “Gene expression profiling identifies clinically relevant subtypes of prostate cancer,” Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. 3, pp. 811–816, 2004. View at Publisher · View at Google Scholar · View at Scopus
8. M. B. Eisen, P. T. Spellman, P. O. Brown, and D. Botstein, “Cluster analysis and display of genome-wide expression patterns,” Proceedings of the National Academy of Sciences of the United States of America, vol. 95, no. 25, pp. 14863–14868, 1998. View at Publisher · View at Google Scholar · View at Scopus
9. T. Schlitt and A. Brazma, “Current approaches to gene regulatory network modelling,” BMC Bioinformatics, vol. 8, supplement 6, article S9, 2007. View at Publisher · View at Google Scholar · View at Scopus
10. H. Hache, H. Lehrach, and R. Herwig, “Reverse engineering of gene regulatory networks: a comparative study,” Eurasip Journal on Bioinformatics and Systems Biology, vol. 2009, Article ID 617281, 2009. View at Publisher · View at Google Scholar · View at Scopus
11. W. P. Lee and W. S. Tzou, “Computational methods for discovering gene networks from expression data,” Briefings in Bioinformatics, vol. 10, no. 4, pp. 408–423, 2009. View at Publisher · View at Google Scholar · View at Scopus
12. C. Sima, J. Hua, and S. Jung, “Inference of gene regulatory networks using time-series data: a survey,” Current Genomics, vol. 10, no. 6, pp. 416–429, 2009. View at Publisher · View at Google Scholar · View at Scopus
13. J. M. Stuart, E. Segal, D. Koller, and S. K. Kim, “A gene-coexpression network for global discovery of conserved genetic modules,” Science, vol. 302, no. 5643, pp. 249–255, 2003. View at Publisher · View at Google Scholar · View at Scopus
14. A. A. Margolin, I. Nemenman, K. Basso et al., “ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context,” BMC Bioinformatics, vol. 7, supplement 1, article S7, 2006. View at Publisher · View at Google Scholar · View at Scopus
15. E. R. Dougherty, S. Kim, and Y. Chen, “Coefficient of determination in nonlinear signal processing,” Signal Processing, vol. 80, no. 10, pp. 2219–2235, 2000. View at Publisher · View at Google Scholar · View at Scopus
16. S. Kim, E. R. Dougherty, Y. Chen et al., “Multivariate measurement of gene expression relationships,” Genomics, vol. 67, no. 2, pp. 201–209, 2000. View at Publisher · View at Google Scholar
17. X. Chen, M. Chen, and K. Ning, “BNArray: an R package for constructing gene regulatory networks from microarray data by using Bayesian network,” Bioinformatics, vol. 22, no. 23, pp. 2952–2954, 2006. View at Publisher · View at Google Scholar · View at Scopus
18. A. V. Werhli, M. Grzegorczyk, and D. Husmeier, “Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks,” Bioinformatics, vol. 22, no. 20, pp. 2523–2531, 2006. View at Publisher · View at Google Scholar · View at Scopus
19. I. Shmulevich, E. R. Dougherty, S. Kim, and W. Zhang, “Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks,” Bioinformatics, vol. 18, no. 2, pp. 261–274, 2002. View at Scopus
20. P. Sumazin, X. Yang, H.-S. Chiu et al., “An extensive MicroRNA-mediated network of RNA-RNA interactions regulates established oncogenic pathways in glioblastoma,” Cell, vol. 147, no. 2, pp. 370–381, 2011. View at Publisher · View at Google Scholar
21. Y. Tay, L. Kats, L. Salmena et al., “Coding-independent regulation of the tumor suppressor PTEN by competing endogenous mRNAs,” Cell, vol. 147, no. 2, pp. 344–357, 2011. View at Publisher · View at Google Scholar
22. D. P. Bartel, “MicroRNAs: target recognition and regulatory functions,” Cell, vol. 136, no. 2, pp. 215–233, 2009. View at Publisher · View at Google Scholar · View at Scopus
23. D. Yue, J. Meng, M. Lu, C. L. P. Chen, M. Guo, and Y. Huang, “Understanding MicroRNA regulation: a computational perspective,” IEEE Signal Processing Magazine, vol. 29, no. 1, Article ID 6105465, pp. 77–88, 2012. View at Publisher · View at Google Scholar
24. M. W. Jones-Rhoades and D. P. Bartel, “Computational identification of plant MicroRNAs and their targets, including a stress-induced miRNA,” Molecular Cell, vol. 14, no. 6, pp. 787–799, 2004. View at Publisher · View at Google Scholar · View at Scopus
25. D. Hanahan and R. A. Weinberg, “The hallmarks of cancer,” Cell, vol. 100, no. 1, pp. 57–70, 2000. View at Scopus
26. S. Y. Chun, C. Johnson, J. G. Washburn, M. R. Cruz-Correa, D. T. Dang, and L. H. Dang, “Oncogenic KRAS modulates mitochondrial metabolism in human colon cancer cells by inducing HIF-1α and HIF-2α target genes,” Molecular Cancer, vol. 9, article 293, 2010. View at Publisher · View at Google Scholar · View at Scopus
27. N. J. Hudson, A. Reverter, and B. P. Dalrymple, “A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation,” PLoS Computational Biology, vol. 5, no. 5, Article ID e1000382, 2009. View at Publisher · View at Google Scholar · View at Scopus
28. I. Stelniec-Klotz, S. Legewie, O. Tchernitsa et al., “Reverse engineering a hierarchical regulatory network downstream of oncogenic KRAS,” Molecular Systems Biology, vol. 8, Article ID 601, 2012. View at Publisher · View at Google Scholar
29. C. Shen, Y. Huang, Y. Liu et al., “A modulated empirical Bayes model for identifying topological and temporal estrogen receptor α regulatory networks in breast cancer,” BMC Systems Biology, vol. 5, article 67, 2011. View at Publisher · View at Google Scholar · View at Scopus
30. C. A. Wilson and J. Dering, “Recent translational research: microarray expression profiling of breast cancer. Beyond classification and prognostic markers?” Breast Cancer Research, vol. 6, no. 5, pp. 192–200, 2004. View at Publisher · View at Google Scholar · View at Scopus
31. H. E. Cunliffe, M. Ringnér, S. Bilke et al., “The gene expression response of breast cancer to growth regulators: patterns and correlation with tumor expression profiles,” Cancer Research, vol. 63, no. 21, pp. 7158–7166, 2003. View at Scopus
32. J. Frasor, F. Stossi, J. M. Danes, B. Komm, C. R. Lyttle, and B. S. Katzenellenbogen, “Selective estrogen receptor modulators: discrimination of agonistic versus antagonistic activities by gene expression profiling in breast cancer cells,” Cancer Research, vol. 64, no. 4, pp. 1522–1533, 2004. View at Publisher · View at Google Scholar · View at Scopus
33. L. J. van't Veer, H. Dai, M. J. van de Vijver et al., “Gene expression profiling predicts clinical outcome of breast cancer,” Nature, vol. 415, no. 6871, pp. 530–536, 2002. View at Publisher · View at Google Scholar · View at Scopus
34. S. A. Kauffman, The Origins of Order : Self-Organization and Selection in Evolution, Oxford University Press, New York, NY, USA, 1993.
35. J. D. Allen, Y. Xie, M. Chen, L. Girard, and G. Xiao, “Comparing statistical methods for constructing large scale gene networks,” PLoS ONE, vol. 7, no. 1, Article ID e29348, 2012. View at Publisher · View at Google Scholar
36. Y. Huang, I. M. Tienda-Luna, and Y. Wang, “Reverse engineering gene regulatory networks: a survey of statistical models,” IEEE Signal Processing Magazine, vol. 26, no. 1, pp. 76–97, 2009. View at Publisher · View at Google Scholar · View at Scopus
37. F. Crick, “Central dogma of molecular biology,” Nature, vol. 227, no. 5258, pp. 561–563, 1970. View at Publisher · View at Google Scholar · View at Scopus
38. A. Hamilton and M. Piccart, “The contribution of molecular markers to the prediction of response in the treatment of breast cancer: a review of the literature on HER-2, p53 and BCL-2,” Annals of Oncology, vol. 11, no. 6, pp. 647–663, 2000. View at Publisher · View at Google Scholar · View at Scopus
39. C. Sotiriou, S. Y. Neo, L. M. McShane et al., “Breast cancer classification and prognosis based on gene expression profiles from a population-based study,” Proceedings of the National Academy of Sciences of the United States of America, vol. 100, no. 18, pp. 10393–10398, 2003. View at Publisher · View at Google Scholar · View at Scopus
40. T. Sørlie, C. M. Perou, R. Tibshirani et al., “Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 19, pp. 10869–10874, 2001. View at Publisher · View at Google Scholar · View at Scopus
41. J. S. Carroll, C. A. Meyer, J. Song et al., “Genome-wide analysis of estrogen receptor binding sites,” Nature Genetics, vol. 38, no. 11, pp. 1289–1297, 2006. View at Publisher · View at Google Scholar
42. K. Basso, A. A. Margolin, G. Stolovitzky, U. Klein, R. Dalla-Favera, and A. Califano, “Reverse engineering of regulatory networks in human B cells,” Nature Genetics, vol. 37, no. 4, pp. 382–390, 2005. View at Publisher · View at Google Scholar · View at Scopus
43. K. C. Liang and X. Wang, “Gene regulatory network reconstruction using conditional mutual information,” Eurasip Journal on Bioinformatics and Systems Biology, vol. 2008, Article ID 253894, 2008. View at Publisher · View at Google Scholar · View at Scopus
44. K. Wang, B. C. Bisikirska, M. J. Alvarez et al., “Genome-wide identification of post-translational modulators of transcription factor activity in human B cells,” Nature Biotechnology, vol. 27, no. 9, pp. 829–837, 2009. View at Publisher · View at Google Scholar
45. M. Hansen, L. Everett, L. Singh, and S. Hannenhalli, “Mimosa: mixture model of co-expression to detect modulators of regulatory interaction,” Algorithms for Molecular Biology, vol. 5, no. 1, article 4, 2010. View at Publisher · View at Google Scholar · View at Scopus
46. O. Babur, E. Demir, M. Gönen, C. Sander, and U. Dogrusoz, “Discovering modulators of gene expression,” Nucleic Acids Research, vol. 38, no. 17, Article ID gkq287, pp. 5648–5656, 2010. View at Publisher · View at Google Scholar · View at Scopus
47. T. Shimamura, S. Imoto, Y. Shimada et al., “A novel network profiling analysis reveals system changes in epithelial-mesenchymal transition,” PLoS ONE, vol. 6, no. 6, Article ID e20804, 2011. View at Publisher · View at Google Scholar · View at Scopus
48. H. Y. Wu, et al., “A modulator based regulatory network for ERalpha signaling pathway,” BMC Genomics, vol. 13, Supplement 6, article S6, 2012.
49. K.-K. Yan, W. Hwang, J. Qian et al., “Construction and analysis of an integrated regulatory network derived from High-Throughput sequencing data,” PLoS Computational Biology, vol. 7, no. 11, Article ID e1002190, 2011. View at Publisher · View at Google Scholar
50. M. Flores and Y. Huang, “TraceRNA: a web based application for ceRNAs prediction,” in Proceedings of the IEEE Genomic Signal Processing and Statistics Workshop (GENSIPS '12), 2012.
51. S. D. Hsu, F. M. Lin, W. Y. Wu et al., “MiRTarBase: a database curates experimentally validated microRNA-target interactions,” Nucleic Acids Research, vol. 39, no. 1, pp. D163–D169, 2011. View at Publisher · View at Google Scholar · View at Scopus
52. H. Liu, D. Yue, Y. Chen, S. J. Gao, and Y. Huang, “Improving performance of mammalian microRNA target prediction,” BMC Bioinformatics, vol. 11, article 476, 2010. View at Publisher · View at Google Scholar · View at Scopus
53. Y. Dong, et al., “A Bayesian decision fusion approach for microRNA target prediction,” BMC Genomics, vol. 13, 2012.
54. J. A. Asm and M. Montague, “Models for Metasearch,” in Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 276–284, la, New Orleans, La, USA, 2001. View at Publisher · View at Google Scholar