{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007950012,0.0006466417,0.0007997353,0.0007045671,0.0005295802,0.001190866,0.0008398676,0.0007047659,0.002453978],"category_scores_gemma":[0.0004831543,0.000354556,0.0004900664,0.0005707722,0.000402133,0.0002744345,0.0009524299,0.001145514,0.002213376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004496207,"about_ca_system_score_gemma":0.0004144615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008783969,"about_ca_topic_score_gemma":0.003699179,"domain_scores_codex":[0.9988967,0.00012523,0.00005778556,0.0002379926,0.0005402198,0.0001420402],"domain_scores_gemma":[0.9996662,0.0001066156,0.00004224859,0.00006466793,0.00007153501,0.00004871588],"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":[0.0000234137,0.00002131862,0.0001473647,0.00002771518,0.000007641326,0.00002007163,0.000008291988,0.00009470483,0.9964978,0.0001136384,0.0003523826,0.002685738],"study_design_scores_gemma":[0.00002009344,0.00008443955,0.004435821,0.000004985073,0.00002873508,0.0002632471,0.00002918105,0.003701992,0.9845697,0.0002945145,0.00654916,0.00001813949],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5043949,0.001983786,0.453849,0.00181484,0.0002519738,0.001034075,0.01339388,0.008610994,0.01466654],"genre_scores_gemma":[0.750503,0.002261658,0.2137756,0.001267203,0.0001003748,0.0008493454,0.01296036,0.001117532,0.01716484],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002453978,"threshold_uncertainty_score":0.008209407,"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."}}