Abstract
Reconstructing gene regulatory network (GRN) from time-series expression data has become increasingly popular since time course data contain temporal information about gene regulation. A typical microarray gene expression data contain expressions of thousands of genes but the number of time samples is usually very small. Therefore, inferring a GRN from such a high-dimensional expression data poses a major challenge. This paper proposes a tree based ensemble of random forests in a multivariate auto-regression framework to tackle this problem. The efficacy of the proposed approach is demonstrated on synthetic time-series datasets and Saccharomyces cerevisiae (Yeast) microarray gene expression data with 9-genes. The performance is comparable or better than GRN generated using dynamic Bayesian networks and ordinary differential equations (ODE) model.
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Husmeier, D.: Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic bayesian networks. Bioinformatics 19(17), 2271–2282 (2003)
Bornholdt, S.: Boolean network models of cellular regulation: prospects and limitations. Journal of the Royal Society Interface 5(suppl. 1), S85–S94 (2008)
Li, P., Zhang, C., Perkins, E.J., Gong, P., Deng, Y.: Comparison of probabilistic boolean network and dynamic bayesian network approaches for inferring gene regulatory networks. BMC Bioinformatics 8(suppl. 7), S13 (2007)
Filkov, V.: Identifying gene regulatory networks from gene expression data. Handbook of Computational Molecular Biology, 27-1 (2005)
Liu, B., Thiagarajan, P.S., Hsu, D.: Probabilistic approximations of signaling pathway dynamics. In: Degano, P., Gorrieri, R. (eds.) CMSB 2009. LNCS (LNBI), vol. 5688, pp. 251–265. Springer, Heidelberg (2009)
Kim, S.Y., Imoto, S., Miyano, S.: Inferring gene networks from time series microarray data using dynamic bayesian networks. Briefings in Bioinformatics 4(3), 228–235 (2003)
Friedman, N., Murphy, K., Russell, S.: Learning the structure of dynamic probabilistic networks. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 139–147. Morgan Kaufmann Publishers Inc. (1998)
Zoppoli, P., Morganella, S., Ceccarelli, M.: TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach. Bmc Bioinformatics 11(1), 154 (2010)
Fujita, A., Sato, J., Garay-Malpartida, H., Yamaguchi, R., Miyano, S., Sogayar, M., Ferreira, C.: Modeling gene expression regulatory networks with the sparse vector autoregressive model. BMC Systems Biology 1, Â 39 (2007)
Rajapakse, J.C., Mundra, P.A.: Stability of building gene regulatory networks with sparse autoregressive models. BMC Bioinformatics 12(suppl. 13), S17 (2011)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Strobl, C., Boulesteix, A.L., Kneib, T., Augustin, T., Zeileis, A.: Conditional variable importance for random forests. BMC Bioinformatics 9(1), 307 (2008)
Cutler, A., Cutler, D.R., Stevens, J.R.: Tree-based methods. High-Dimensional Data Analysis in Cancer Research, 1–19 (2009)
Boulesteix, A.L., Janitza, S., Kruppa, J., König, I.R.: Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics (2012)
Huynh-Thu, V.A., Irrthum, A., Wehenkel, L., Geurts, P.: Inferring regulatory networks from expression data using tree-based methods. PLoS One 5(9), e12776 (2010)
Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and regression trees. Chapman & Hall/CRC (1984)
Pagano, M., Gauvreau, K., Pagano, M.: Principles of biostatistics. Duxbury Pacific Grove^ eCA CA (2000)
Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
Marbach, D., Schaffter, T., Mattiussi, C., Floreano, D.: Generating realistic in silico gene networks for performance assessment of reverse engineering methods. Journal of Computational Biology 16(2), 229–239 (2009)
Simon, I., Barnett, J., Hannett, N., Harbison, C.T., Rinaldi, N.J., Volkert, T.L., Wyrick, J.J., Zeitlinger, J., Gifford, D.K., Jaakkola, T.S., et al.: Serial regulation of transcriptional regulators in the yeast cell cycle. Cell 106(6), 697–708 (2001)
Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., Anders, K., Eisen, M.B., Brown, P.O., Botstein, D., Futcher, B.: Comprehensive identification of cell cycle–regulated genes of the yeast saccharomyces cerevisiae by microarray hybridization. Molecular Biology of the Cell 9(12), 3273–3297 (1998)
Husmeier, D.: Inferring dynamic bayesian networks with mcmc (2003), http://www.bioss.ac.uk/~dirk/software/DBmcmc/index.html
Bansal, M., Della Gatta, G., Di Bernardo, D.: Inference of gene regulatory networks and compound mode of action from time course gene expression profiles. Bioinformatics 22(7), 815–822 (2006)
Haifen, C., Maduranga, D., Mundra, P., Zheng, J.: Integrating epigenetic prior in dynamic bayesian network for gene regulatory network inference. In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (accepted, 2013)
Mundra, P., Niranjan, M., Welsch, R., Zheng, J., Rajapakse, J.: Inferring time-delayed gene regulatory networks using cross-correlation and sparse regression. In: 9th International Symposium on Bioinformatics Research and Applications (accepted, 2013)
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Maduranga, D.A.K., Zheng, J., Mundra, P.A., Rajapakse, J.C. (2013). Inferring Gene Regulatory Networks from Time-Series Expressions Using Random Forests Ensemble. In: Ngom, A., Formenti, E., Hao, JK., Zhao, XM., van Laarhoven, T. (eds) Pattern Recognition in Bioinformatics. PRIB 2013. Lecture Notes in Computer Science(), vol 7986. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39159-0_2
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