A Deep Learning-Based Model for Gene Regulatory Network Inference

Gene regulatory networks (GRNs) model the transcriptional regulations in biological organisms, offering essential dissections of cellular processes and functions. A wide range of approaches has been developed to reconstruct GRNs from gene expression data. The molecular interactions between regulators and downstream target genes are indicated by nodes connected with edges in the GRNs. Due to the complexity of regulatory mechanisms, GRN inference remains challenging. In this study, we propose a novel deep-learning-based method for network inference. We compared our model with four state-of-the-art GRN inference approaches, including GENIE3, GRNBoost2, KBoost, and DeepSEM. Five datasets from the DREAM4 challenge were used in comparisons. We assessed the model performance according to the weight matrix generated by each method and the golden standard networks. The weight matrix represented the predicted probability of regulations between transcription factors (Ti's) and target genes. Then, the area under the receiver operator curve (AUROC) was computed and employed for the performance assessment. The results demonstrate that our model achieves comparable performance to the best-performing model. This work establishes a novel network inference method for enhancing our understanding of gene regulation.