Reconstruction of gene regulatory networks (GRNs) is an important step for understanding the complex regulatory mechanisms within the cell. Many modeling approaches have been introduced to find the causal relationship between genes using expression data. However, they have been suffering from high dimensionality - large number of genes but a small number of samples -, over fitting, and heavy computation time. In this work 1, we present a novel method, namely SETNET, to improve the stability and accuracy of GRN inference using ensemble techniques. For a given target gene, SETNET extract an ensemble of regulation networks from discretized expression data instead of a single one. Inferred networks are then assessed by ranking individual regulation relationships using a regression based technique and continuous expression data. Evaluation on DREAM5 data demonstrates that SETNET is efficient, specially when operating on a small data set.
['I. Chhbil', 'Mohamed Elati', 'Céline Rouveirol', 'Guillaume Santini']
SetNet: Ensemble Method Techniques for Learning Regulatory Networks