{"id":"W4410212252","doi":"10.1186/s13059-025-03586-7","title":"Predicting adenine base editing efficiencies in different cellular contexts by deep learning","year":2025,"lang":"en","type":"article","venue":"Genome biology","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acuitas Therapeutics (Canada)","funders":"National Institute of Allergy and Infectious Diseases; Functional Genomics Center Zurich; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Staatssekretariat für Bildung, Forschung und Innovation; Université de Genève; National Institutes of Health; National Science Foundation","keywords":"Biology; Genome editing; Computational biology; Guide RNA; In vivo; Base (topology); RNA editing; CRISPR; Computer science; Genetics; Messenger RNA; Gene","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":[0.0005333117,0.0007538131,0.0004206562,0.0003915564,0.0001674355,0.0006346161,0.0005299796,0.0008244326,0.0006036153],"category_scores_gemma":[0.001214803,0.0002681107,0.0005743232,0.0002587,0.0002752258,0.0004485293,0.0003800184,0.001068471,0.0001933389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009589453,"about_ca_system_score_gemma":0.0006257739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004487141,"about_ca_topic_score_gemma":0.005994315,"domain_scores_codex":[0.999869,0.00002566223,0.000006927081,0.00004912671,0.00002352553,0.00002586449],"domain_scores_gemma":[0.9994811,0.0003439127,0.00005774851,0.0000250065,0.00005550438,0.0000366829],"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":[0.0001155248,0.0001040057,0.006776107,0.00005840578,0.00006416732,0.00004193465,0.00001423111,0.9635698,0.009009156,0.0003637302,0.0005578662,0.01932505],"study_design_scores_gemma":[0.000002574778,0.00002771882,0.0003824089,0.000002448092,0.000005987674,0.000006564049,0.000002900073,0.9960652,0.00308835,0.0003348377,0.0000781277,0.000002875966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.819038,0.0007641239,0.1750136,0.0003958065,0.00004186545,0.000054664,0.001164355,0.001520589,0.002006941],"genre_scores_gemma":[0.9636582,0.0002836132,0.03319278,0.0001362729,0.00001265095,0.00007429618,0.001454087,0.00004558471,0.001142441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004487141,"threshold_uncertainty_score":0.00892204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003540530828262324,"score_gpt":0.256867700341476,"score_spread":0.2533271695132137,"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."}}