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

Authors:
D. A. K. Maduranga;Jie Zheng;Piyushkumar A. Mundra;Jagath C. Rajapakse
Affiliations:
Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore;Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore,Genome Institute of Singapore, Singapore;Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore;Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore,Singapore-MIT Alliance, Singapore,Department of Biological Engineering, Massachusetts In ...
Venue:
PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
Year:
2013

Citing 4
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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.