DMVD-GRN: Denoised Multi-View Decoding with Structure-Aware Link Scoring for Gene Regulatory Network Inference

Abstract

Reconstruction of gene regulatory networks (GRNs) is essential for uncovering regulatory relationships between transcription factors (TFs) and target genes. With advances in single-cell RNA sequencing (scRNA-seq), cell-type-specific GRN inference has become an important direction in systems biology; however, existing deep learning methods still struggle with noisy expression, symmetric link scoring that cannot capture regulatory direction, and difficulty separating co-expression and co-regulation from direct regulation. Built upon the variational autoencoder (VAE)-graph attention network (GAT) framework of GRANet [1], we propose DMVD-GRN with enhanced VAE multi-view denoising, skew-symmetric directed decoding, and structure-aware joint decoding, integrating expression correlation, second-order adjacency, and Jaccard co-regulation priors. On 14 tasks of the STRING benchmark, DMVD-GRN achieves superior AUROC and AUPRC, with more pronounced AUPRC gains, thereby improving identification of true regulatory edges under extreme class imbalance.