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The difficulty stems partly from the sparsity and noise inherent in single-cell data and partly from the complexity of dynamic combinatorial regulation mediated by transcription factors. In this work, we introduce LogicSR, a computational framework that reconstructs gene regulatory networks from single-cell gene expression data with high accuracy by integrating the mechanistic interpretability of Boolean logical models with the equation-discovery capabilities of symbolic regression. It incorporates prior knowledge into a multi-objective Monte Carlo tree search (MCTS) framework, leveraging it to ensure biological plausibility and accelerate the search for optimal governing equations. LogicSR outperforms existing methods on both synthetic and real-world benchmark datasets. When applied to a human embryonic stem cell dataset, it demonstrates superior performance in elucidating complex combinatorial TF\u2013target gene regulations and identifying key regulators.<\/jats:p>","DOI":"10.1093\/bib\/bbaf621","type":"journal-article","created":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T15:04:53Z","timestamp":1763737493000},"source":"Crossref","is-referenced-by-count":1,"title":["LogicSR: prior-guided symbolic regression for gene regulatory network inference from single-cell transcriptomics data"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5304-2672","authenticated-orcid":false,"given":"Dezhen","family":"Zhang","sequence":"first","affiliation":[{"name":"Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University , Jinan, Shandong 250061 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