{"id":"W2518901523","doi":"10.1038/nature19356","title":"High-throughput discovery of novel developmental phenotypes","year":2016,"lang":"en","type":"article","venue":"Nature","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1294,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children; Toronto Centre for Phenogenomics; Mount Sinai Hospital","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Cancer Institute; National Eye Institute; Medical Research Council; Centre National de la Recherche Scientifique; Bundesministerium für Bildung und Forschung; Université de Strasbourg; Institut National de la Santé et de la Recherche Médicale; National Human Genome Research Institute; Wellcome Trust; Agence Nationale de la Recherche; Genome Canada; National Institutes of Health; Ontario Genomics; Cancer Research UK; PHENOMIN; Government of Canada; INFRAFRONTIER","keywords":"Gene knockout; Biology; Phenotype; Genetics; Gene; Gene targeting; Penetrance; Computational biology; Knockout mouse; Genetic screen; Candidate gene; Lethal allele","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":[0.0006220064,0.0005618904,0.0007270947,0.000578353,0.0004066311,0.001592837,0.0006494645,0.0005059179,0.002070416],"category_scores_gemma":[0.0004196065,0.0004021673,0.0006745221,0.0005581154,0.0002998138,0.0006135064,0.0006857805,0.001704061,0.001298306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005994496,"about_ca_system_score_gemma":0.000396662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004981203,"about_ca_topic_score_gemma":0.001735091,"domain_scores_codex":[0.9996393,0.00003487046,0.00001371388,0.00006335868,0.0002053592,0.00004342978],"domain_scores_gemma":[0.9996877,0.0001420082,0.00003597581,0.00005415834,0.00004256787,0.00003763863],"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":[0.00006379389,0.00005038929,0.000205751,0.00006933134,0.00002369,0.00005047591,0.00001398589,0.0002770874,0.9846166,0.001006064,0.0009140124,0.01270871],"study_design_scores_gemma":[0.00003666446,0.00007870379,0.002233437,0.000008687717,0.0000500836,0.0002021569,0.00002089897,0.005441329,0.9778906,0.0009654741,0.013054,0.00001791097],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5262589,0.009583835,0.4256527,0.002617133,0.0005774223,0.0005575802,0.01060056,0.004925311,0.01922638],"genre_scores_gemma":[0.6544964,0.01243484,0.2976368,0.0008430448,0.0002513788,0.0006347129,0.01196761,0.0007388962,0.02099627],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002070416,"threshold_uncertainty_score":0.006926239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0044937526918827,"score_gpt":0.2448637395123112,"score_spread":0.2403699868204285,"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."}}