Inferring gene regulatory networks from time-series expressions using random forests ensemble

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7986 LNBI, Issue , 2013, Pages 13-22

  Maduranga, D A K ^a   Zheng, Jie ^a,b   Mundra, Piyushkumar A ^a   Rajapakse, Jagath C ^a,c,d  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b GENOME INSTITUTE OF SINGAPORE   (Singapore)

^c SINGAPORE MIT ALLIANCE   (Singapore)

^d MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

gene regulation; Gene regulatory networks; multivariate auto regression; Random forests; regression trees; time series gene expression data

Indexed keywords

BAYESIAN NETWORKS; BIOINFORMATICS; DECISION TREES; ORDINARY DIFFERENTIAL EQUATIONS; PATTERN RECOGNITION; RANDOM FORESTS; TIME SERIES; YEAST;

AUTO REGRESSION; DYNAMIC BAYESIAN NETWORKS; GENE REGULATORY NETWORKS; MICROARRAY GENE EXPRESSION DATA; ORDINARY DIFFERENTIAL EQUATION (ODE); REGRESSION TREES; TEMPORAL INFORMATION; TIME-SERIES GENE EXPRESSION DATA;

GENE EXPRESSION;

DIFFERENTIAL EQUATIONS; FORESTS; GENES; INFORMATION RETRIEVAL; PATTERN RECOGNITION; REGRESSION ANALYSIS; TIME SERIES ANALYSIS; YEASTS;

EID: 84880705559     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-39159-0_2     Document Type: Conference Paper

References (25)

1. Husmeier, D.: Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic bayesian networks. Bioinformatics 19(17), 2271-2282 (2003)
2. Bornholdt, S.: Boolean network models of cellular regulation: prospects and limitations. Journal of the Royal Society Interface 5(suppl. 1), S85-S94 (2008)
3. 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)
4. Filkov, V.: Identifying gene regulatory networks from gene expression data. Handbook of Computational Molecular Biology, 27-1 (2005)
5. 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)
6. 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)
7. 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)
8. 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)
9. 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, 39 (2007)
10. Rajapakse, J.C., Mundra, P.A.: Stability of building gene regulatory networks with sparse autoregressive models. BMC Bioinformatics 12(suppl. 13), S17 (2011)
11. Breiman, L.: Random forests. Machine Learning 45(1), 5-32 (2001)
12. Strobl, C., Boulesteix, A.L., Kneib, T., Augustin, T., Zeileis, A.: Conditional variable importance for random forests. BMC Bioinformatics 9(1), 307 (2008)
13. Cutler, A., Cutler, D.R., Stevens, J.R.: Tree-based methods. High-Dimensional Data Analysis in Cancer Research, 1-19 (2009)
14. 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)
15. 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)
16. Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and regression trees. Chapman & Hall/CRC (1984)
17. Pagano, M., Gauvreau, K., Pagano, M.: Principles of biostatistics. Duxbury Pacific Grove eCA CA (2000)
18. Barab́asi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509-512 (1999)
19. 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)
20. 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)
21. 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)
22. Husmeier, D.: Inferring dynamic bayesian networks with mcmc (2003), http://www.bioss.ac.uk/~dirk/software/DBmcmc/index.html
23. 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)
24. 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)
25. Mundra, P., Niranjan, M., Welsch, R., Zheng, J., Rajapakse, J.: Inferring timedelayed gene regulatory networks using cross-correlation and sparse regression. In: 9th International Symposium on Bioinformatics Research and Applications (accepted, 2013)