Performance and Improvement of Tree-Based Methods for Gene Regulatory Network Reconstruction

  • Ming Shi
  • Yan-Wen ChongEmail author
  • Shao-Ming Pan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9771)


Computational reconstruction of gene regulatory networks (GRNs) from gene expression data is of great importance in systems biology. Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge aims to evaluate the success of computational GRN inference algorithm on benchmarks of simulated data. Tree-based methods, such as Random Forest, infer true regulators of a target gene in a feature selection way andexhibitcompetitiveperformance. GENIE3 algorithm is a Random Forest-based algorithm and was winner of the DREAM4 InSilico Multifactorial challenge. In this paper, we further investigated the performance of tree-based algorithms for GRN inference. Experimental results showed that GENIE3 loses robustness on small-scale heterozygous knock-down datasets, and a slightly modified version of GENIE3 algorithm mGENIE3 was provided. Experiments conducted on simulation and real gene expression datasets show superior performance of mGENIE3.


Gene expression data Gene regulatory network DREAM challenge Tree-based method Random Forest Modified version 



This work has been partially supported by the National Natural Science Foundation of China (Grant Nos. 61572372 and 41271398), LIESMARS Special Research Funding, and also partially supported by the Fund of SAST (Project No. SAST201425). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conflict of Interest.

The authors declare that there is no conflict of interest regarding the publication of this article.


  1. 1.
    Zhou, T., Wang, Y.L.: Causal relationship inference for a large-scale cellular network. Bioinformatics 26(16), 2020–2028 (2010)CrossRefGoogle Scholar
  2. 2.
    Zhang, X.J., Liu, K.Q., Liu, Z.P., Duval, B., Richer, J.M., Zhao, X.M., Hao, J.K., Chen, L.N.: NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference. Bioinformatics 29(1), 106–113 (2013)CrossRefGoogle Scholar
  3. 3.
    Wang, Y.Y., Li, Z.G., Chen, T., Zhao, X.M.: Understanding the aristolochic acid toxicities in rat kidneys with regulatory networks. IET Syst. Biol. 9(4), 141–146 (2015)CrossRefGoogle Scholar
  4. 4.
    Dover, G.A.: The origins of order-self-organization and selection in evolution- Kauffman, SA. Nature 365(6448), 704–706 (1993)CrossRefGoogle Scholar
  5. 5.
    De Jong, H.: Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol. 9(1), 67–103 (2002)CrossRefGoogle Scholar
  6. 6.
    McAdams, H.H., Arkin, A.: Stochastic mechanisms in gene expression. Proc. Natl. Acad. Sci. USA 94(3), 814–819 (1997)CrossRefGoogle Scholar
  7. 7.
    Hasty, J., McMillen, D., Isaacs, F., Collins, J.J.: Computational studies of gene regulatory networks: in numero molecular biology. Nat. Rev. Genet. 2(4), 268–279 (2001)CrossRefGoogle Scholar
  8. 8.
    Wille, A., Buhlmann, P.: Low-order conditional independence graphs for inferring genetic networks. Stat. Appl. Genet. Mol. Biol. Mol. 5(1) (2006)Google Scholar
  9. 9.
    De La Fuente, A., Bing, N., Hoeschele, I., Mendes, P.: Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 20(18), 3565–3574 (2004)CrossRefGoogle Scholar
  10. 10.
    Tenenhaus, A., Guillemot, V., Gidrol, X., Frouin, V.: Gene association networks from microarray data using a regularized estimation of partial correlation based on PLS regression. IEEE/ACM Trans. Comput. Biol. Bioinform. 7(2), 251–262 (2010)CrossRefGoogle Scholar
  11. 11.
    Peng, J., Wang, P., Zhou, N.F., Zhu, J.: Partial correlation estimation by joint sparse regression models. J. Am. Stat. Assoc. 104(486), 735–746 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Liaw, A., Wiener, M.: Classification and regression by Random Forest. R news 2(3), 18–22 (2002)Google Scholar
  13. 13.
    Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3–42 (2006)CrossRefzbMATHGoogle Scholar
  14. 14.
    Drucker, H., Cortes, C.: Boosting decision trees. Adv. Neural Inf. Process. Syst. 1059, 479–485 (1996)Google Scholar
  15. 15.
    Marbach, D., Costello, J.C., Kuffner, R., Vega, N.M., Prill, R.J., Camacho, D.M., Allison, K.R., Kellis, M., Collins, J.J., Stolovitzky, G., DREAM5 Consortium: Wisdom of crowds for robust gene network inference. Nat. Methods 9(8), 796–804 (2012)CrossRefGoogle Scholar
  16. 16.
    James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning. Springer, New York (2013)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote SensingWuhan UniversityWuhanChina

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