scGPL：融合图与成对学习从 scRNA-seq 数据推断基因调控网络
Bridging Graph and Pairwise Learning for Gene Regulatory Network Inference from scRNA-seq Data
AI 导读
研究者提出 scGPL，一个端到端统一框架，用于从 scRNA-seq 数据推断基因调控网络（GRN），同时建模全局调控拓扑与局部 TF-靶基因相互作用。该框架从全局调控图提取宏观特征，并通过动态重要性引导的子图采样捕捉细粒度局部依赖。在七个基准数据集上取得 SOTA 表现，较最强基线最高提升 13.7%，源代码已在 GitHub 开源。
正文 · 原文
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 (this https URL), and the datasets and versioned releases are archived at this https URL for reproducibility.
| Comments: | Accepted to Bioinformatics |
| Subjects: | Genomics (q-bio.GN); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.13801 [q-bio.GN] |
| (or arXiv:2611.13801v1 [q-bio.GN] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13801 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jiaxin Qi [view email]
[v1]
Tue, 10 Nov 2026 09:34:28 UTC (3,259 KB)
来源：arXiv cs.AI · arxiv.org