{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00303907,0.0009517196,0.001057256,0.001029862,0.0003659354,0.000920761,0.00102509,0.0005842441,0.0008906497],"category_scores_gemma":[0.00322863,0.0004763658,0.0005546573,0.0006065637,0.0003479445,0.000378041,0.0007808711,0.0006654261,0.0004479704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009365794,"about_ca_system_score_gemma":0.0008707709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001472884,"about_ca_topic_score_gemma":0.003353439,"domain_scores_codex":[0.997647,0.0005260482,0.000276658,0.000449816,0.0009167228,0.0001838621],"domain_scores_gemma":[0.9984864,0.0006519323,0.0002321255,0.0001964059,0.0003195979,0.0001135653],"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":[0.0002552847,0.000202375,0.002987854,0.000196554,0.00009791648,0.0001277156,0.00004263701,0.02097762,0.9596248,0.0003937009,0.0002660874,0.01482746],"study_design_scores_gemma":[0.00005358908,0.0007263492,0.005869944,0.0000185126,0.0001130829,0.0002208431,0.0000483113,0.04180641,0.9479305,0.0001880292,0.002983772,0.00004060692],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7778077,0.0009673534,0.2117199,0.0001406697,0.00004131417,0.001268067,0.003859442,0.002488133,0.001707491],"genre_scores_gemma":[0.7628672,0.0009867719,0.2275805,0.0001392837,0.000006428601,0.001049637,0.005302171,0.0005962665,0.001471786],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.00303907,"threshold_uncertainty_score":0.01607239,"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."}}