{"id":"W4405419739","doi":"10.1038/s41587-024-02512-9","title":"CRISPR-StAR enables high-resolution genetic screening in complex in vivo models","year":2024,"lang":"en","type":"article","venue":"Nature Biotechnology","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital","funders":"Terry Fox Research Institute; Krembil Foundation; Staatssekretariat für Bildung, Forschung und Innovation; Österreichischen Akademie der Wissenschaften; Canada Research Chairs","keywords":"CRISPR; Computational biology; Biology; Cas9; Genome; Bottleneck; Guide RNA; Genome editing; Genetics; Gene; 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":[0.001442907,0.0006699817,0.000996583,0.0007967288,0.0003486765,0.00103206,0.0008145117,0.0007507213,0.001587656],"category_scores_gemma":[0.0007956999,0.0005203828,0.0006445902,0.0004623928,0.000525747,0.0004664346,0.001261856,0.00126241,0.001002434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005561408,"about_ca_system_score_gemma":0.0005167188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001028098,"about_ca_topic_score_gemma":0.002814177,"domain_scores_codex":[0.998894,0.0001982896,0.00008727126,0.0002542476,0.0004722436,0.0000938654],"domain_scores_gemma":[0.9989004,0.0003521503,0.0002284766,0.0002958944,0.0001035768,0.0001194637],"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":[0.00004278093,0.00002622179,0.0002541543,0.00004869469,0.00001528448,0.00006251652,0.00001843584,0.0009535658,0.9950406,0.0004661917,0.0002046474,0.002866849],"study_design_scores_gemma":[0.00001555954,0.0001569448,0.001694006,0.000009844734,0.00002343845,0.0003435624,0.00001835998,0.01118314,0.9810907,0.0004731387,0.004969003,0.0000222585],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.404682,0.0009951006,0.5790585,0.0003721917,0.0001028924,0.0003127973,0.003427758,0.006293846,0.004754901],"genre_scores_gemma":[0.7043705,0.001753119,0.2804161,0.0002717529,0.00002615078,0.000509134,0.005038771,0.001127629,0.006486901],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001587656,"threshold_uncertainty_score":0.007630944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008567904010227318,"score_gpt":0.2794367990180069,"score_spread":0.2708688950077796,"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."}}