{"id":"W2974223043","doi":"10.1038/s41551-019-0454-8","title":"High-throughput genome-wide phenotypic screening via immunomagnetic cell sorting","year":2019,"lang":"en","type":"article","venue":"Nature Biomedical Engineering","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":82,"is_retracted":false,"has_abstract":false,"ca_institutions":"Muscular Dystrophy Canada; University of Toronto","funders":"Canadian Institutes of Health Research; Hospital for Sick Children","keywords":"Cell sorting; Genetic screen; CRISPR; Phenotype; Computational biology; High-throughput screening; Biology; Cas9; Cell; Genome; Sorting; Cell biology; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000208569,0.0003154044,0.0002692943,0.0001216224,0.00005121137,0.00003221704,0.0003463785,0.0005926399,0.0001147934],"category_scores_gemma":[0.0001138523,0.0003192698,0.0001372579,0.0002481646,0.00003523549,0.000007235477,0.0002022406,0.0006289931,0.00005336932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002733274,"about_ca_system_score_gemma":0.00003465843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001110238,"about_ca_topic_score_gemma":9.275182e-7,"domain_scores_codex":[0.9982352,0.00001392848,0.0003522217,0.0005181425,0.0003100758,0.0005703979],"domain_scores_gemma":[0.99918,0.0000376331,0.00006570742,0.0004666708,0.00004874071,0.0002012081],"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":[0.00002043012,0.00003338027,0.000498715,0.0001321383,0.00005405818,0.000008888146,0.00003159246,0.01831471,0.9780833,0.00002709926,0.0001811803,0.002614486],"study_design_scores_gemma":[0.005161975,0.001354131,0.05414473,0.000262924,0.0001512313,0.0001199609,0.0001355289,0.05948914,0.5704918,0.00006139238,0.3057919,0.002835354],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7081809,0.007538285,0.2822987,0.0001129356,0.001206157,0.0002546087,0.00000926364,0.0001110202,0.0002881463],"genre_scores_gemma":[0.9730506,0.00007098709,0.02542746,0.0002111343,0.0005747639,0.0000134268,0.000203449,0.00007776063,0.0003704211],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4075915,"threshold_uncertainty_score":0.9999259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.001752262028730535,"score_gpt":0.2176603905439917,"score_spread":0.2159081285152611,"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."}}