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This study introduces STREAM, a novel method that leverages a Steiner forest problem model, a hybrid biclustering pipeline, and submodular optimization to infer eGRNs from jointly profiled single-cell transcriptome and chromatin accessibility data. Compared to existing methods, STREAM demonstrates enhanced performance in terms of TF recovery, TF\u2013enhancer linkage prediction, and enhancer\u2013gene relation discovery. Application of STREAM to an Alzheimer's disease dataset and a diffuse small lymphocytic lymphoma dataset reveals its ability to identify TF-enhancer\u2013gene relations associated with pseudotime, as well as key TF-enhancer\u2013gene relations and TF cooperation underlying tumor cells.<\/jats:p>","DOI":"10.1093\/bib\/bbae369","type":"journal-article","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T01:54:20Z","timestamp":1722477260000},"source":"Crossref","is-referenced-by-count":11,"title":["Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data"],"prefix":"10.1093","volume":"25","author":[{"given":"Yang","family":"Li","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics , College of Medicine, , Columbus, OH 43210, United States"},{"name":"The Ohio State University , College of Medicine, , Columbus, OH 43210, United 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