{"id":"W4285228088","doi":"10.1007/978-1-0716-2257-5_19","title":"High-Throughput Gene Mutagenesis Screening Using Base Editing","year":2022,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; PROTEO; Université Laval","funders":"Canadian Institutes of Health Research","keywords":"CRISPR; Genome editing; Mutagenesis; Cas9; Computational biology; Base (topology); Biology; Genetics; Gene; Mutation; Computer science","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":[0.001368719,0.001520824,0.001576235,0.001004018,0.0006691945,0.001090113,0.001558951,0.001061055,0.003280883],"category_scores_gemma":[0.0007020188,0.0006933817,0.001075285,0.0008579219,0.0004838352,0.0003828731,0.001022449,0.001565205,0.00288549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005219877,"about_ca_system_score_gemma":0.0005693685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000899974,"about_ca_topic_score_gemma":0.002417059,"domain_scores_codex":[0.9979063,0.0003605915,0.0001759889,0.0004014873,0.0009024831,0.0002532046],"domain_scores_gemma":[0.9993956,0.0001884222,0.00008633965,0.0001574891,0.00009819065,0.00007393076],"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":[0.00003720216,0.00004374328,0.00008206199,0.00005075697,0.00001382545,0.00004836821,0.0000108808,0.0001300162,0.9964697,0.0001344018,0.0001825852,0.002796594],"study_design_scores_gemma":[0.00001711373,0.00009959936,0.000545504,0.000005028014,0.00003400501,0.0002217913,0.000008777512,0.00126077,0.994534,0.00009189315,0.003164415,0.000017156],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4412161,0.002145059,0.5228602,0.000764364,0.0003458328,0.002201743,0.008502075,0.01054251,0.01142202],"genre_scores_gemma":[0.6950806,0.002294314,0.2697979,0.0003782413,0.00003846015,0.001036733,0.01172305,0.001391879,0.01825885],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003280883,"threshold_uncertainty_score":0.01097566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02438659164100575,"score_gpt":0.4156694056366679,"score_spread":0.3912828139956622,"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."}}