{"id":"W4285045763","doi":"10.1016/j.ymthe.2022.07.010","title":"A precise and efficient adenine base editor","year":2022,"lang":"en","type":"article","venue":"Molecular Therapy","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institute of Genetics","funders":"National Institutes of Health","keywords":"Bystander effect; Genome editing; Computational biology; Mutation; Gene; Biology; Genetics; Genome","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":[0.001187552,0.000771652,0.0007861576,0.0007280799,0.00131767,0.002035213,0.001383575,0.001968239,0.01945774],"category_scores_gemma":[0.001745133,0.000609096,0.0003608574,0.0005442486,0.0006898075,0.001349025,0.001350787,0.003531593,0.00717676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004676502,"about_ca_system_score_gemma":0.0008964296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002512316,"about_ca_topic_score_gemma":0.0006165354,"domain_scores_codex":[0.9987937,0.0002004488,0.000114257,0.0003357122,0.0004115973,0.000144177],"domain_scores_gemma":[0.9984828,0.0004139541,0.0002077625,0.0003686908,0.0003089931,0.0002179339],"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":[0.0004084497,0.0001125169,0.0002373354,0.0002622383,0.00002053578,0.0006477492,0.00013784,0.0006581531,0.8766042,0.03538384,0.01541616,0.07011084],"study_design_scores_gemma":[0.00003005608,0.0001145263,0.00009284237,0.00001274543,0.00001705108,0.0007555046,0.00003420738,0.002023429,0.8778346,0.003212688,0.1158367,0.00003567553],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1101278,0.004418667,0.7220923,0.009296112,0.02253607,0.0007141579,0.00248961,0.01827645,0.1100489],"genre_scores_gemma":[0.4792033,0.002263386,0.3187965,0.002426848,0.001848179,0.0002817404,0.001147609,0.002158673,0.1918738],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01945774,"threshold_uncertainty_score":0.06509256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004072216163870348,"score_gpt":0.2546094067775029,"score_spread":0.2505371906136326,"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."}}