{"id":"W4304975999","doi":"10.1186/s13073-022-01118-7","title":"Mendelian gene identification through mouse embryo viability screening","year":2022,"lang":"en","type":"article","venue":"Genome Medicine","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital; SickKids Foundation; Toronto Centre for Phenogenomics; Hospital for Sick Children","funders":"Medical Research Council; Cancer Research UK; National Institutes of Health; National Institute for Health and Care Research; Department of Health and Social Care; Wellcome Trust","keywords":"Gene; Biology; Phenotype; Genetics; Loss function; Disease; Lethal allele; Human genetics; Genome; Mendelian inheritance; OMIM : Online Mendelian Inheritance in Man; Computational biology; Medicine; Pathology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001104254,0.0007456263,0.0004621274,0.003272085,0.0004106547,0.000589263,0.0008484841,0.0005285114,0.005637678],"category_scores_gemma":[0.00116849,0.000368847,0.0006020668,0.0007810932,0.0004093791,0.0002451438,0.0007879794,0.0007384353,0.002680117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003308491,"about_ca_system_score_gemma":0.000357425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007775808,"about_ca_topic_score_gemma":0.001415826,"domain_scores_codex":[0.9992052,0.0001100466,0.00007172392,0.0002117058,0.0003282992,0.00007314929],"domain_scores_gemma":[0.9990049,0.0003674213,0.0002320079,0.0001763314,0.0001338604,0.00008555964],"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":[0.0003788931,0.0000534344,0.007069693,0.0001765611,0.00007118552,0.001068317,0.00009746903,0.0006328894,0.9505895,0.002863779,0.001506329,0.03549206],"study_design_scores_gemma":[0.0001443568,0.0007327687,0.05255852,0.0001324346,0.0002592939,0.007563275,0.0001247612,0.01735534,0.8620021,0.003647978,0.05538785,0.00009139858],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4406449,0.004391728,0.5058319,0.0005460397,0.0001510002,0.001231449,0.02569034,0.005952992,0.01555968],"genre_scores_gemma":[0.6480285,0.004492202,0.2982194,0.0003880243,0.00006440655,0.001403348,0.0321495,0.001242173,0.0140125],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005637678,"threshold_uncertainty_score":0.01885992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01646012864950215,"score_gpt":0.2578127980911077,"score_spread":0.2413526694416056,"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."}}