Hybrid method inference for the construction of cooperative regulatory network in human

Reconstruction of large scale gene regulatory networks (GRNs in the following) 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, overfitting, heavy computation time and low interpretability. We have previously proposed an algorithm LICORN, which uses the discrete expression data to find cooperative regulation relationships that are out of the scope of most GRN inference methods. However, as many other methods, LICORN suffers from a large number of false positives. We propose here a hybrid inference method H-Licorn that combines Licorn with a numerical selection step, expressed as a linear regression problem, that effectively complements the discrete search of Licorn. We evaluate a bootstrapped version of H-LICORN on the in silico DREAM5 dataset and show that H-LICORN has significantly higher performance than LICORN, and is competitive or outperforms state of the art GRN inference algorithms, especially when operating on small data sets. We also applied H-LICORN on a real dataset of human bladder cancer and show that it performs better than other methods in finding candidate regulatory interactions. In particular, solely based on gene expression data, H-LICORN is able to identify experimentally validated regulator cooperative relationships involved in cancer.