{"id":"W2953212411","doi":"10.1101/117341","title":"Evaluation and Design of Genome-wide CRISPR/Cas9 Knockout Screens","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Amgen (Canada); Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital; University of Toronto","funders":"Canadian Cancer Society Research Institute; Cancer Prevention and Research Institute of Texas; University of Texas MD Anderson Cancer Center; Canada Research Chairs; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"CRISPR; Gene knockout; Computational biology; Biology; Gene; Functional genomics; Cas9; Genome editing; Genome; Guide RNA; Genetics; Human genome; Genomics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001309486,0.0004119336,0.0004078682,0.0001292226,0.0001253884,0.00009680454,0.0004436162,0.0005250294,0.00001493264],"category_scores_gemma":[0.0007082638,0.0004658973,0.0001115502,0.00006253169,0.0001310142,0.00000684688,0.0005409148,0.0002699463,0.000005282625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004999271,"about_ca_system_score_gemma":0.0004812367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002934157,"about_ca_topic_score_gemma":0.000003003378,"domain_scores_codex":[0.9979466,0.0001450641,0.0003879502,0.0007896981,0.0003810545,0.000349665],"domain_scores_gemma":[0.997308,0.00002557437,0.0003471563,0.001436325,0.0007099436,0.0001730064],"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":[0.00002986345,0.00003835308,0.002476237,0.0001946486,0.0002137861,0.000004512464,0.000006328439,0.00446509,0.9923472,0.00001060699,0.0001941023,0.00001925163],"study_design_scores_gemma":[0.0006319611,0.0001386726,0.1280869,0.0001645788,0.0003452809,4.027088e-8,0.000002786067,0.003087417,0.8642623,0.000003758832,0.002630744,0.0006456368],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8136106,0.01421175,0.1700664,0.00007778678,0.0005608193,0.001210295,0.0001806955,0.00005408554,0.00002756955],"genre_scores_gemma":[0.9894534,0.001393741,0.008548948,0.0000389781,0.0003495659,0.0001156608,0.000001980061,0.0000897372,0.000007930661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1758429,"threshold_uncertainty_score":0.9997793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02269493981156739,"score_gpt":0.2836480456598549,"score_spread":0.2609531058482875,"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."}}