Reverse Engineering Gene Regulatory Networks Using Sampling and Boosting Techniques

Reverse engineering gene regulatory networks (GRNs), also known as network inference, refers to the process of reconstructing GRNs from gene expression data. Biologists model a GRN as a directed graph in which nodes represent genes and links show regulatory relationships between the genes. By predicting the links to infer a GRN, biologists can gain a better understanding of regulatory circuits and functional elements in cells. Existing supervised GRN inference methods work by building a feature-based classifier from gene expression data and using the classifier to predict the links in GRNs. Observing that GRNs are sparse graphs with few links between nodes, we propose here to use under-sampling, over-sampling and boosting techniques to enhance the prediction performance. Experimental results on different datasets demonstrate the good performance of the proposed approach and its superiority over the existing methods.