{"id":"W2138164217","doi":"10.15252/msb.20145216","title":"Measuring error rates in genomic perturbation screens: gold standards for human functional genomics","year":2014,"lang":"en","type":"article","venue":"Molecular Systems Biology","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":464,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; Princess Margaret Cancer Centre; University Health Network; Ontario Institute for Cancer Research; University of Toronto","funders":"Ontario Institute for Cancer Research; Ontario Ministry of Research and Innovation; Canadian Institute for Advanced Research","keywords":"CRISPR; Biology; Computational biology; Functional genomics; RNA interference; Gene; Genomics; Genetics; Genome; RNA","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":[],"consensus_categories":[],"category_scores_codex":[0.000617057,0.0001905567,0.000233227,0.0001025238,0.00006018403,0.00002513782,0.0001442503,0.0002329716,0.000003165939],"category_scores_gemma":[0.0001249685,0.0001961118,0.00009953742,0.00005564571,0.00004125756,0.00000233302,0.00006027734,0.00007631644,0.000002711609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007996051,"about_ca_system_score_gemma":0.00006366352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005481147,"about_ca_topic_score_gemma":0.00007536563,"domain_scores_codex":[0.9987484,0.0001080307,0.0003263754,0.0004213981,0.00008420133,0.00031165],"domain_scores_gemma":[0.9994263,0.00001693721,0.00007827084,0.0002858821,0.0001342183,0.00005837789],"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":[0.00004526802,0.0000144935,0.001429962,0.00005572761,0.00004404069,6.975786e-7,0.000019573,0.01149382,0.9838194,0.002400073,0.0001684359,0.0005084664],"study_design_scores_gemma":[0.005015148,0.001588275,0.01338384,0.0001130056,0.00007977304,0.00007975383,0.0002122188,0.03009067,0.7173905,0.00127228,0.2293742,0.001400313],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6248495,0.001890816,0.3721692,0.00003465693,0.0003215262,0.0003485142,0.00004447443,0.00001464189,0.0003266914],"genre_scores_gemma":[0.9981691,0.00001893155,0.000579868,0.00008408779,0.0003545535,0.0001295043,0.0003883264,0.00003987763,0.0002357057],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3733197,"threshold_uncertainty_score":0.7997208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02152405074075081,"score_gpt":0.3064075218976376,"score_spread":0.2848834711568868,"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."}}