# Bridging Graph and Pairwise Learning for Gene Regulatory Network Inference from scRNA-seq Data

**Authors:** Hang Li, Jiaxin Qi, Yan Cui, Yuhua Zheng, Jianqiang Huang  
**Published:** 10 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.13801) · [PDF](https://arxiv.org/pdf/2611.13801) · [Code](https://github.com/talent-hang/scGPL) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.13801.html)

## Key numbers

- **13.7%**: with up to 13.7% improvement over the strongest baseline

## Abstract

Motivation: Gene regulatory networks (GRNs) capture the underlying interactions through which transcription factors (TFs) regulate genes. Based on gene expression data, existing GRN inference approaches generally fall into two categories: graph-based methods, which model the GRN as a whole graph, and pairwise-based methods, which decompose the GRN into individual TF-target gene pairs for modeling. However, each approach exhibits limitations that are precisely the strengths of its counterpart. Graph-based methods tend to overfit due to their reliance on a single training graph, whereas pairwise-based methods benefit from abundant TF-target training pairs. In contrast, pairwise-based methods overlook the global topological structure, which is essential for comprehensively capturing regulatory mechanisms. Results: To address these limitations, we propose scGPL, an end-to-end unified framework that jointly models global regulatory topology and local interactions. scGPL extracts macro-level features from the global regulatory graph and introduces a dynamic, importance-guided subgraph sampling mechanism to capture fine-grained local dependencies. By fusing global representations with subgraph-enhanced local features, our framework bridges the gap between the two paradigms, incorporating essential whole-graph topological dependencies while retaining the data abundance advantage of pairwise methods. Extensive experiments on seven benchmark datasets demonstrate that scGPL consistently achieves state-of-the-art performance, with up to 13.7% improvement over the strongest baseline, validating the effectiveness of our integrative design. Availability and implementation: The source code is maintained on GitHub (https://github.com/talent-hang/scGPL), and the datasets and versioned releases are archived at https://doi.org/10.5281/zenodo.21608485 for reproducibility.

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