{"id":"W4211219428","doi":"10.1038/s43586-022-00098-7","title":"High-content CRISPR screening","year":2022,"lang":"en","type":"article","venue":"Nature Reviews Methods Primers","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":253,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; University of Toronto","funders":"Common Fund; National Institute of Biomedical Imaging and Bioengineering; National Institute of Diabetes and Digestive and Kidney Diseases; Li Ka Shing Foundation; Canadian Institutes of Health Research; European Commission; National Institutes of Health; National Science Foundation","keywords":"CRISPR; Computational biology; Biology; Trans-activating crRNA; Multiplex; Genome editing; Drug discovery; Gene; Genetics; Bioinformatics","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":[0.001863352,0.001627447,0.001462838,0.001705127,0.0009423766,0.001837837,0.002006236,0.001849407,0.01875803],"category_scores_gemma":[0.001497116,0.001174834,0.001123796,0.001019648,0.0008199051,0.0007267257,0.001802321,0.002901812,0.02131867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008653979,"about_ca_system_score_gemma":0.0009025998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000724929,"about_ca_topic_score_gemma":0.002818234,"domain_scores_codex":[0.9966719,0.0005133977,0.000220629,0.0006855831,0.001444584,0.000463789],"domain_scores_gemma":[0.9988294,0.0003543445,0.0001138957,0.0003940858,0.0001818583,0.0001263599],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00012264,0.00009510413,0.0003905648,0.0004917461,0.00005279815,0.0001875254,0.00005018015,0.0004091899,0.9482394,0.003227171,0.01324544,0.03348827],"study_design_scores_gemma":[0.00004370275,0.0001522225,0.001586593,0.00006195474,0.00006592363,0.0007875329,0.00003034074,0.001644319,0.9127862,0.001604146,0.08116947,0.00006748561],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07586873,0.005427831,0.750029,0.002499331,0.001467008,0.002532673,0.03170119,0.04011203,0.0903623],"genre_scores_gemma":[0.3226668,0.006687928,0.4859735,0.004324937,0.0002511159,0.00260052,0.06023053,0.00710615,0.1101585],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01875803,"threshold_uncertainty_score":0.06275183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03925068954497576,"score_gpt":0.4228328634316131,"score_spread":0.3835821738866373,"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."}}