Inductive inference of gene regulatory network using supervised and semi-supervised graph neural networks

Abstract

Discovering gene regulatory relationships and reconstructing gene regulatory networks (GRN) based on gene expression data is a classical, long-standing computational challenge in bioinformatics. Computationally inferring a possible regulatory relationship between two genes can be formulated as a link prediction problem between two nodes in a graph. Graph neural network (GNN) provides an opportunity to construct GRN by integrating topological neighbor propagation through the whole gene network. We propose an end-to-end gene regulatory graph neural network (GRGNN) approach to reconstruct GRNs from scratch utilizing the gene expression data, in both a supervised and a semi-supervised framework. To get better inductive generalization capability, GRN inference is formulated as a graph classification problem, to distinguish whether a subgraph centered at two nodes contains the link between the two nodes. A linked pair between a transcription factor (TF) and a target gene, and their neighbors are labeled as a positive subgraph, while an unlinked TF and target gene pair and their neighbors are labeled as a negative subgraph. A GNN model is constructed with node features from both explicit gene expression and graph embedding. We demonstrate a noisy starting graph structure built from partial information, such as Pearson's correlation coefficient and mutual information can help guide the GRN inference through an appropriate ensemble technique. Furthermore, a semi-supervised scheme is implemented to increase the quality of the classifier. When compared with established methods, GRGNN achieved state-of-the-art performance on the DREAM5 GRN inference benchmarks. GRGNN is publicly available at https://github.com/juexinwang/GRGNN.

Keywords: Gene regulatory; Graph neural networks; Inductive learning; Machine learning.

Graphical abstract

Fig. 1

GRGNN scheme. Noisy starting skeletons derived from Pearson’s correlation and mutual information are used to generate the enclosed positive subgraph centering with A and B, and the negative graph centering with C and D. Graph neural networks as the agents are learned independently. An ensemble classifier is built upon these agents and used for the link prediction through graph classification.

Fig. 2

ROC curve and Precision-Recall curve on balanced training and testing.

Fig. 3

Performances of GRGNN in different numbers of hops.