Inferring Gene Regulatory Networks from Time-Series Expressions Using Random Forests Ensemble

  • D. A. K. Maduranga
  • Jie Zheng
  • Piyushkumar A. Mundra
  • Jagath C. Rajapakse
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7986)


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.


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


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • D. A. K. Maduranga
    • 1
  • Jie Zheng
    • 1
    • 2
  • Piyushkumar A. Mundra
    • 1
  • Jagath C. Rajapakse
    • 1
    • 3
    • 4
  1. 1.Bioinformatics Research Center, School of Computer EngineeringNanyang Technological UniversitySingapore
  2. 2.Genome Institute of SingaporeSingapore
  3. 3.Singapore-MIT AllianceSingapore
  4. 4.Department of Biological EngineeringMassachusetts Institute of TechnologyUSA

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