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

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.KeywordsGene regulatory networkstime-series gene expression datagene regulationRandom forestsmultivariate auto-regressionregression trees