Learning Gene Regulatory Networks using Graph Granger Causality

Abstract

Interacting systems such as gene regulatory networks have the ability to respond to in- dividual component changes, propagate these changes throughout the network, and affect the temporal trajectories of other network elements. Causality techniques are frequently employed to investigate the interconnection between variables in complex dynamical sys- tems. However, the vast majority of causality models are rooted in regression techniques such as Vector Autoregression Models and Bootstrap Elastic net regression from Time Se- ries framework, and there is very limited research in the space of deep learning, particularly graph neural networks. In this paper, we explore in more depth the concept of Granger causality in deep learning and propose Granger causality deep learning framework using graphs convolutions, LSTM, and nonlinear penalties for the objective of learning causal relationships between temporal elements in gene regulatory networks. The deep learn- ing architecture proposed here for studying causality in dynamic networks has achieved high results on simulated networks as well as on more challenging Dream3 gene regulatory networks time-series datasets.