{"id":"W2950114027","doi":"10.1534/genetics.119.302089","title":"<i>beditor</i> : A Computational Workflow for Designing Libraries of Guide RNAs for CRISPR-Mediated Base Editing","year":2019,"lang":"en","type":"article","venue":"Genetics","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; PROTEO","funders":"Canadian Institutes of Health Research","keywords":"CRISPR; Biology; Workflow; Genetics; Guide RNA; Computational biology; Base (topology); Genome editing; World Wide Web; Computer science; Database; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001604772,0.0001457114,0.0001675335,0.00004199371,0.00004904639,0.00002290752,0.0001526763,0.0001269685,0.000009321433],"category_scores_gemma":[0.0001773765,0.0001594026,0.0001123783,0.00006537607,0.00003411835,0.00000350661,0.00006107967,0.00004232094,0.000002435472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006313157,"about_ca_system_score_gemma":0.00009919455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001124327,"about_ca_topic_score_gemma":0.000003523343,"domain_scores_codex":[0.999055,0.00001333016,0.0002981942,0.0002600323,0.0001174822,0.0002560096],"domain_scores_gemma":[0.9993042,0.000144131,0.0000954607,0.0001998895,0.0001904909,0.00006580146],"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":[0.0000917654,0.00004014755,0.002750302,0.0002395065,0.00009395584,3.687811e-7,0.0001120831,0.1158821,0.8411025,0.0001023891,0.03623365,0.003351275],"study_design_scores_gemma":[0.001400509,0.0004770565,0.0003991329,0.00004593902,0.00004221488,0.000003463496,0.0001496017,0.03512959,0.8586974,0.0005725063,0.1028055,0.0002771451],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1148043,0.0009904263,0.8821905,0.0001148501,0.001005602,0.0005925482,0.0001685896,0.0000191649,0.0001140207],"genre_scores_gemma":[0.604504,0.00003600372,0.3925665,0.0001736605,0.001731978,0.0000744237,0.0006080631,0.00005342014,0.0002520279],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4896996,"threshold_uncertainty_score":0.650025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009182172179352601,"score_gpt":0.2785637669332133,"score_spread":0.2693815947538607,"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."}}