{"id":"W2938052030","doi":"10.1101/612887","title":"Scalable, FACS-Free Genome-Wide Phenotypic Screening","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; University of Toronto","funders":"Sickkids Research Institute; Hospital for Sick Children","keywords":"Phenotype; CRISPR; Genome; Genetic screen; Computational biology; Cas9; Phenotypic screening; Biology; Cell sorting; Cell; Function (biology); Sorting; 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":[0.001072545,0.0006887472,0.0006501668,0.0006240695,0.0003807383,0.0009918239,0.0007156033,0.0006608057,0.005151455],"category_scores_gemma":[0.0005960634,0.0003694893,0.0004290849,0.0004248022,0.0004157925,0.000370242,0.001087381,0.001145971,0.003567484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000500951,"about_ca_system_score_gemma":0.0004946358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007835213,"about_ca_topic_score_gemma":0.001536583,"domain_scores_codex":[0.9990253,0.0001224279,0.00006443077,0.0001911365,0.0004934893,0.0001031231],"domain_scores_gemma":[0.9995574,0.0001489285,0.00005901433,0.0001126433,0.0000754794,0.00004661407],"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":[0.00004169089,0.00002969109,0.0001631006,0.00003147165,0.000008435227,0.00004652104,0.000009656493,0.0003570306,0.9956912,0.0002420241,0.0007076112,0.002671611],"study_design_scores_gemma":[0.00003364522,0.00009028398,0.001608531,0.000006306466,0.00001570595,0.0001989989,0.00002432648,0.004846136,0.9829533,0.0003511415,0.009852032,0.00001957514],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4175112,0.001037139,0.5252922,0.001175942,0.0002497415,0.001909651,0.02491353,0.01182254,0.01608809],"genre_scores_gemma":[0.6382645,0.001243416,0.3125415,0.000563782,0.00006117255,0.002039633,0.02051168,0.002059208,0.0227151],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005151455,"threshold_uncertainty_score":0.01723331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008721810389380361,"score_gpt":0.2284857365722252,"score_spread":0.2197639261828449,"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."}}