CoDiffGRN: Rethinking Gene Regulatory Network Inference via the BEELINE-KGC Benchmark and Co-evolutionary Discrete Diffusion

2026-07-14

AI Summary

This study addresses the limited generalization of existing gene regulatory network inference methods under inductive settings and the absence of evaluation benchmarks aligned with biological experimental needs. The authors reformulate the task as a ranking-centric inductive graph completion problem and introduce the first co-evolutionary discrete diffusion framework that jointly models discrete gene expression states and regulatory interactions. To enhance training efficiency, they propose a TF-ALL subgraph sampling strategy. Furthermore, they establish BEELINE-KGC, the first inductive benchmark focused on discovering high-confidence novel regulatory relationships. Experimental results demonstrate that the proposed method significantly outperforms current state-of-the-art models on BEELINE-KGC, and ablation studies confirm the effectiveness of each component.

Abstract

Inferring gene regulatory networks (GRNs) from single-cell transcriptomic data is crucial for biological discovery, yet existing approaches suffer from a fundamental misalignment with real-world needs. Researchers typically seek a small set of high-confidence regulatory interactions for experimental validation, often involving previously unseen genes. However, current benchmarks rely on transductive splits with global classification metrics, while prevailing models struggle to generalize under inductive settings. To bridge this gap, we reformulate GRN inference as an inductive, ranking-centric graph completion problem and introduce \textbf{\benchmark}, a new benchmark that incorporates an inductive gene-holdout split together with knowledge graph completion metrics to better evaluate top-ranked predictions. Building on this, we propose \textbf{\method}, the first co-evolutionary discrete diffusion framework that jointly models biologically coherent discretized gene expression states and regulatory interactions for robust inductive generalization and improved top-ranked regulatory discovery. We further introduce TF-ALL Subgraph Sampling (TASS) for scalable training. Extensive experiments on {\benchmark} show that {\method} establishes new state-of-the-art performance, significantly outperforming existing methods in novel regulatory discovery, and ablation studies further verify the effectiveness of our design.

Problem

• gene regulatory network
• inductive inference
• single-cell transcriptomics
• knowledge graph completion
• regulatory discovery

Innovation

• inductive GRN inference
• discrete diffusion
• co-evolutionary modeling
• knowledge graph completion
• top-ranked regulatory discovery