{"id":"W4288446620","doi":"10.1038/s41551-022-00911-4","title":"Efficient in vivo base editing via single adeno-associated viruses with size-optimized genomes encoding compact adenine base editors","year":2022,"lang":"en","type":"article","venue":"Nature Biomedical Engineering","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":187,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Montreal Clinical Research Institute","funders":"National Institute of General Medical Sciences; National Eye Institute; National Human Genome Research Institute; National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research; National Institute of Allergy and Infectious Diseases; Bill and Melinda Gates Foundation; National Science Foundation; National Institutes of Health; U.S. Department of Health and Human Services; Howard Hughes Medical Institute","keywords":"Base (topology); Genome; In vivo; Biology; Encoding (memory); Computer science; Computational biology; Genetics; Gene; Mathematics; Neuroscience","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004100435,0.0004036408,0.0003639766,0.0002088327,0.0001121076,0.0005427485,0.0003017549,0.0003969016,0.0008371545],"category_scores_gemma":[0.0004222408,0.0002368124,0.0002623096,0.0001065736,0.0003042175,0.0003228155,0.000415721,0.0008142173,0.0003475238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002261599,"about_ca_system_score_gemma":0.0001655446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001784923,"about_ca_topic_score_gemma":0.0004093658,"domain_scores_codex":[0.9996106,0.0000671122,0.00003920486,0.0001045371,0.0001303821,0.00004821517],"domain_scores_gemma":[0.9996767,0.00007497206,0.0001231017,0.00005129822,0.00002729843,0.00004661914],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004347312,0.0000118837,0.00004734876,0.00001632239,0.000005533152,0.00001951116,0.000007607287,0.0001745719,0.9980391,0.00019156,0.0000285148,0.001414617],"study_design_scores_gemma":[0.000009591707,0.0001160782,0.0002430289,0.000002692469,0.00001009063,0.0002060889,0.000004690652,0.001448383,0.9958853,0.00006678778,0.002001003,0.000006188768],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9044189,0.001375634,0.09044184,0.0001192875,0.0001002174,0.0001212255,0.0004245265,0.0005867655,0.002411588],"genre_scores_gemma":[0.9340326,0.0007053745,0.06044567,0.00005239906,0.00002344267,0.00007083672,0.0005014377,0.0001399031,0.004028345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008371545,"threshold_uncertainty_score":0.002800524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004183485916595092,"score_gpt":0.2376368045420254,"score_spread":0.2334533186254303,"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."}}