@inproceedings{qiu-etal-2025-grnformer,
    title = "{GRNF}ormer: A Biologically-Guided Framework for Integrating Gene Regulatory Networks into {RNA} Foundation Models",
    author = "Qiu, Mufan  and
      Hu, Xinyu  and
      Zhan, Fengwei  and
      Yun, Sukwon  and
      Peng, Jie  and
      Zhang, Ruichen  and
      Kailkhura, Bhavya  and
      Yang, Jiekun  and
      Chen, Tianlong",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.findings-acl.196/",
    pages = "3805--3819",
    ISBN = "979-8-89176-256-5",
    abstract = "Foundation models for single-cell RNA sequencing (scRNA-seq) have shown promising capabilities in capturing gene expression patterns. However, current approaches face critical limitations: they ignore biological prior knowledge encoded in gene regulatory relationships and fail to leverage multi-omics signals that could provide complementary regulatory insights. In this paper, we propose GRNFormer, a new framework that systematically integrates multi-scale Gene Regulatory Networks (GRNs) inferred from multi-omics data into RNA foundation model training. Our framework introduces two key innovations. First, we introduce a pipeline for constructing hierarchical GRNs that capture regulatory relationships at both cell-type-specific and cell-specific resolutions. Second, we design a structure-aware integration framework that addresses the information asymmetry in GRNs through two technical advances: (1) A graph topological adapter using multi-head cross-attention to weight regulatory relationships dynamically, and (2) a novel edge perturbation strategy that perturb GRNs with biologically-informed co-expression links to augment graph neural network training. Comprehensive experiments have been conducted on three representative downstream tasks across multiple model architectures to demonstrate the effectiveness of GRNFormer. It achieves consistent improvements over state-of-the-art (SoTA) baselines: $\mathbf{3.6\\\%}$ increase in drug response prediction correlation, $\mathbf{9.6\\\%}$ improvement in single-cell drug classification AUC, and $\mathbf{1.1\\\%}$ average gain in gene perturbation prediction accuracy."
}