{"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":"codex-gemma-dda1882f352a","candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0001283266,0.0001929661,0.0001819646,0.0003962766,0.00002650753,0.00002101307,0.0002427592,0.001310688,0.00002152528],"category_scores_gemma":[0.00004173563,0.000199531,0.00005453575,0.0004097601,0.00007169157,0.000005088201,0.0001540503,0.0006480089,0.000003879296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002827429,"about_ca_system_score_gemma":0.00003470468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001276915,"about_ca_topic_score_gemma":0.0005487095,"domain_scores_codex":[0.9987249,0.00002659361,0.0002370903,0.0005264426,0.00009839406,0.0003866113],"domain_scores_gemma":[0.9995795,0.00001199348,0.00002044732,0.0003328523,0.00002015841,0.00003498082],"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":[0.00003541557,0.00003170409,0.0002415667,0.00006617789,0.00002678148,0.00007551126,0.0000302377,0.03216009,0.9546261,0.003894648,0.001999602,0.006812148],"study_design_scores_gemma":[0.0008533583,0.000233278,0.004521827,0.0001354408,0.00001939431,0.00009617183,0.0001172351,0.1603713,0.7262236,0.002625723,0.1042084,0.0005943424],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8787262,0.03506639,0.08350933,0.001616333,0.0003620646,0.0002913797,0.00004127815,0.0001592344,0.0002277669],"genre_scores_gemma":[0.9838769,0.0009791084,0.01468216,0.0001271541,0.0001262558,0.00002251626,0.00005151064,0.00003290642,0.0001015189],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2284026,"threshold_uncertainty_score":0.9999858,"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."}}