AI RESEARCH
Prior-Guided Multi-Omic Transformers for Single-Cell Gene Regulatory Network Inference
arXiv CS.LG
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ArXi:2606.00685v1 Announce Type: new Gene regulatory networks (GRNs) capture transcription factor-target interactions and are central to understanding cell-state regulation and disease. Reconstructing GRNs from paired single-cell transcriptomic and chromatin accessibility data is promising but challenging: scATAC is extremely sparse, and most methods rely on fixed peak-to-gene links and weak supervision. We present EpiAwareNet, a prior-guided multi-omic Transformer framework that reconstructs GRNs from paired single-cell data using only lightweight biological priors. In.