{"id":"W4392231300","doi":"10.1016/j.isci.2024.109352","title":"scGREAT: Transformer-based deep-language model for gene regulatory network inference from single-cell transcriptomics","year":2024,"lang":"en","type":"article","venue":"iScience","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Shenzhen Research Institute, City University of Hong Kong; Research Grants Council, University Grants Committee; Innovation and Technology Commission; National Natural Science Foundation of China; City University of Hong Kong","keywords":"Gene regulatory network; Inference; Computer science; Computational biology; Systems biology; Transformer; Gene; Regulation of gene expression; Embedding; Gene expression; Artificial intelligence; Biology; Genetics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005822481,0.001216442,0.0009439416,0.0008111082,0.000354063,0.0008995964,0.002280741,0.001189667,0.003600184],"category_scores_gemma":[0.001522732,0.0005598246,0.002016103,0.0008014037,0.0005586407,0.001114003,0.0009714679,0.002153197,0.001594474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001083006,"about_ca_system_score_gemma":0.00162917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.014184,"about_ca_topic_score_gemma":0.02894865,"domain_scores_codex":[0.9997457,0.00004734584,0.00001370961,0.0001087269,0.00004799732,0.00003653067],"domain_scores_gemma":[0.9995896,0.0002185804,0.00003448143,0.00005529235,0.00007136035,0.00003065935],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003079236,0.0001408924,0.002555983,0.0003636471,0.0003091636,0.0003481892,0.0001305559,0.80215,0.02372485,0.01436038,0.01205846,0.14355],"study_design_scores_gemma":[0.000008550802,0.000017334,0.00008581817,0.000004794708,0.00001194764,0.00002500549,0.000006747013,0.9920303,0.001508493,0.005527167,0.0007673758,0.000006449771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02077544,0.0007918973,0.9627295,0.0002921803,0.00008164682,0.0000586255,0.002666,0.01128807,0.001316545],"genre_scores_gemma":[0.4844541,0.001293399,0.4801948,0.001035109,0.0001130186,0.0004986605,0.02152798,0.001771905,0.009111008],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.014184,"threshold_uncertainty_score":0.02820289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02247016173428946,"score_gpt":0.2495894373720944,"score_spread":0.227119275637805,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}