{"id":"W3029194094","doi":"10.1186/s13059-020-02046-8","title":"MAUDE: inferring expression changes in sorting-based CRISPR screens","year":2020,"lang":"en","type":"article","venue":"Genome biology","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; Klarman Cell Observatory, Broad Institute; National Institutes of Health; National Human Genome Research Institute; Howard Hughes Medical Institute","keywords":"CRISPR; Sorting; Biology; Leverage (statistics); Computational biology; Expression (computer science); Gene expression; Gene; Computer science; Genetics; Artificial intelligence; Algorithm; Programming language","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":[0.00009366651,0.0001465857,0.0001601889,0.00005377008,0.0000320223,0.000007100631,0.0001712851,0.0001723091,0.00004164773],"category_scores_gemma":[0.00008342446,0.0001443559,0.00004583979,0.00009264931,0.00003615312,0.000001127716,0.0001332309,0.0001020917,0.00001141884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008747637,"about_ca_system_score_gemma":0.00002849139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001778227,"about_ca_topic_score_gemma":0.00009401576,"domain_scores_codex":[0.9990752,0.00003930619,0.0001750343,0.000359815,0.00004109299,0.000309524],"domain_scores_gemma":[0.9996312,0.000009252607,0.00004498328,0.00019181,0.00002223301,0.0001005032],"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":[0.00003820639,0.00001172867,0.01368944,0.00002153737,0.000006658702,0.00000356003,0.00006587943,0.002805087,0.9824026,0.00001247947,0.00006980624,0.0008729802],"study_design_scores_gemma":[0.000776844,0.0004846629,0.01826581,0.00001436436,0.000006996798,0.000003320791,0.00008285169,0.001167549,0.9291838,0.00002453519,0.04965568,0.0003336142],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9444362,0.00188343,0.05190234,0.001002411,0.0001166066,0.000191723,0.00002155868,0.00003889914,0.0004068563],"genre_scores_gemma":[0.9967985,0.00008391794,0.001448446,0.0011495,0.0003158481,0.00002340431,0.0001371963,0.00002123502,0.00002192754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05321886,"threshold_uncertainty_score":0.5886662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01773218496093884,"score_gpt":0.318719065549013,"score_spread":0.3009868805880742,"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."}}