{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T15:01:27Z","timestamp":1785250887638,"version":"3.55.0"},"reference-count":0,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:00:00Z","timestamp":1785196800000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Understanding gene regulation at single-cell resolution is crucial for unraveling development, disease, and cellular identity. We introduce single-cell regulatory graph attention network (scReGAT), a deep learning framework that integrates prior knowledge of cis-regulatory element (cRE)-gene and transcription factor-gene interactions to reconstruct cell-specific regulatory networks. Central to scReGAT is a knowledge-guided regulatory graph (kRG), which combines experimentally validated regulatory interactions with cell-resolved chromatin accessibility profiles. These graphs serve as the foundation for training a Graph Attention Network (GAT) to predict gene expression and quantify the contribution of specific regulatory interactions using an interpretable regulatory score for each edge. In benchmarking across five single-cell multi-omics datasets, scReGAT successfully recapitulates known cell-type-specific cRE-gene interactions. In both neuroblastoma and osteogenic differentiation systems, it uncovers dynamic regulatory rewiring that predicts transcriptional transitions. Furthermore, by integrating genome-wide association studies loci from Alzheimer\u2019s disease, multiple sclerosis, and schizophrenia, scReGAT identifies disease-associated cell types and uncovers candidate regulatory mechanisms underlying complex trait associations. These results position scReGAT as a robust and generalizable framework for decoding long-range gene regulation at single-cell resolution. The source code of scReGAT can be accessed at https:\/\/github.com\/TianLab-Bioinfo\/scReGAT\/ and https:\/\/ngdc.cncb.ac.cn\/biocode\/tool\/BT008081.<\/jats:p>","DOI":"10.1093\/gpbjnl\/qzag072","type":"journal-article","created":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T14:38:03Z","timestamp":1785249483000},"source":"Crossref","is-referenced-by-count":0,"title":["scReGAT: Leveraging Knowledge of Regulatory Interactions to Predict Long-range Gene Regulation at Single-cell Resolution"],"prefix":"10.1093","author":[{"given":"Baole","family":"Wen","sequence":"first","affiliation":[{"name":"Fudan University State Key Laboratory of Genetics and Development of Complex Phenotypes, Department of Computational Biology, School of Life Sciences, , Shanghai 200438,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanan","family":"Dang","sequence":"additional","affiliation":[{"name":"Fudan University State Key Laboratory of Genetics and Development of Complex Phenotypes, Department of Computational Biology, School of Life Sciences, , Shanghai 200438,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Fudan University State Key Laboratory of Genetics and Development of Complex Phenotypes, Department of Computational Biology, School of Life Sciences, , Shanghai 200438,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Long","sequence":"additional","affiliation":[{"name":"Fudan University State Key Laboratory of Genetics and Development of Complex Phenotypes, Department of Computational Biology, School of Life Sciences, , Shanghai 200438,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weidong","family":"Tian","sequence":"additional","affiliation":[{"name":"Fudan University State Key Laboratory of Genetics and Development of Complex Phenotypes, Department of Computational Biology, School of Life Sciences, , Shanghai 200438,","place":["China"]},{"name":"Children\u2019s Hospital of Fudan University , Shanghai 201102,","place":["China"]},{"name":"Children\u2019s Hospital of Shandong University , Jinan 250022,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,7,28]]},"container-title":["Genomics, Proteomics &amp; Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/gpb\/advance-article-pdf\/doi\/10.1093\/gpbjnl\/qzag072\/70263856\/qzag072.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/gpb\/advance-article-pdf\/doi\/10.1093\/gpbjnl\/qzag072\/70263856\/qzag072.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T14:38:04Z","timestamp":1785249484000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/gpb\/advance-article\/doi\/10.1093\/gpbjnl\/qzag072\/8744091"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,28]]},"references-count":0,"URL":"https:\/\/doi.org\/10.1093\/gpbjnl\/qzag072","relation":{},"ISSN":["1672-0229","2210-3244"],"issn-type":[{"value":"1672-0229","type":"print"},{"value":"2210-3244","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,28]]},"article-number":"qzag072"}}