GRNAgent: A Multimodal Graph Reasoning Agent for Gene Regulatory Network Inference

Inferring gene regulatory networks (GRNs) from multimodal biological data requires integrating regulatory evidence beyond transcript abundance alone. Regulatory evidence can come from RNA-seq, chromatin accessibility, and transcription factor (TF) motif priors, but these sources are difficult to discover, harmonize, and integrate, and are often incomplete across biological contexts. We propose GRNAgent, an automated multi-agent framework for multi-omic GRN inference that coordinates evidence acquisition, quality control, modality integration, model inference, benchmarking, and post hoc validation. At the core of GRNAgent is Transcription Factor–centered Evidence Adaptive Graph Reasoning (TF-EAGER), a transformer-based graph reasoning model built on TF-centered evidence graphs. For each TF, GRNAgent constructs a local candidate graph whose nodes are candidate target genes and whose edges are annotated with available regulatory evidence, including expression association, motif support, and chromatin accessibility. TF-EAGER converts this local evidence graph into typed evidence tokens and scores each TF–target gene edge by staged cross-attention: first over motif and chromatin accessibility signals, then over expression-derived evidence, and finally over all available signals. This TF-centered evidence-graph formulation couples the automated multi-omic evidence construction with structured graph reasoning over heterogeneous evidence, enabling regulatory edge prediction from partially available biological signals. Across 24 datasets and 10 baselines, GRNAgent achieves the best full-matrix benchmark performance among the evaluated methods, yielding 5–20× improvements in area under the precision-recall curve (AUPRC) and early precision (EP) over the best-performing baselines. Under sampled evaluation, it reaches AUPRC 0.801 in leave-one-TF-out validation and AUPRC 0.705 in unseen cell-type inference, with average EP@10 around 0.81 in the blind setting. Finally, GRNAgent includes a grounded literature verification module, which uses a large language model (LLM) to structure retrieved biological evidence for predicted edges without influencing model training.