CoDiffGRN：通过BEELINE-KGC基准和协同进化离散扩散重新思考基因调控网络推理
CoDiffGRN: Rethinking Gene Regulatory Network Inference via the BEELINE-KGC Benchmark and Co-evolutionary Discrete Diffusion
浏览论文内容
中文总结 AI 辅助
该研究针对单细胞转录组数据推断基因调控网络问题，提出CoDiffGRN方法，将GRN推理转化为归纳图完成问题，引入新基准和协同进化离散扩散框架，实验表明其在新调控发现上性能优越，优于现有方法。
中文摘要 AI 辅助
从单细胞转录组数据推断基因调控网络（GRN）对生物学发现至关重要，但现有方法与实际需求存在根本偏差。研究人员通常寻求少量高置信度调控相互作用用于实验验证，常涉及未见基因。当前基准依赖全局分类指标的转导分割，而主流模型在归纳设置下难以泛化。为弥合差距，我们将GRN推理重新表述为归纳、以排序为中心的图完成问题，并引入\textbf{\benchmark}基准，结合归纳基因保留分割和知识图完成指标以更好评估顶级预测。在此基础上，我们提出\textbf{\method}，首个协同进化离散扩散框架，联合建模生物连贯离散化基因表达状态和调控相互作用以实现稳健归纳泛化和改进顶级调控发现。我们还引入TF-ALL子图采样用于可扩展训练。在\textbf{\benchmark}上的大量实验表明\textbf{\method}建立了新的最优性能，在新调控发现中显著优于现有方法，消融研究进一步验证了我们设计的有效性。
英文摘要
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.
发表机构
- Peking University（北京大学）
- National University of Defense Technology（国防科技大学）
机构由 AI 辅助整理，请以论文原文为准。
