A Multi-Omics Data Integration Framework for Gene Regulatory Network Inference Based on Contrastive Learning

The Gene Regulatory Networks (GRNs) ensure the stability of cellular states, preserving specific phenotypes and functions throughout the differentiation process. However, current tools still need improvement to effectively integrate multi-omics data and infer GRNs for particular cell types. We introduce CLMOGRI, a multi-omics TF-gene regulatory network inference framework based on heterogeneous networks and contrastive learning, designed to integrate multi-omics data for GRN inference. Through random walk techniques, CLMOGRI embeds multi-omics data into a unified feature space and extracts similar features between nodes. It then measures node similarity and predicts node relationships by contrastive learning. Finally, it includes a regulatory network interpreter to identify critical nodes and modules in GRNs, offering an analytical method for understanding complex interactions within biological systems. CLMOGRI surpasses existing baseline methods in terms of Area Under the Precision-Recall Curve (AUPR) and F-Score metrics, indicating its efficacy in capturing multi-omics information for GRN inference. It also reveals vital nodes and modules within the gene regulatory network, improving the interpretability of CLMOGRI and the utility of GRNs.