{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003918359,0.0001310125,0.0001474058,0.00004210555,0.0003036955,0.000009265473,0.0002702049,0.00003826155,0.0004598298],"category_scores_gemma":[0.00005665777,0.0001223265,0.00006724297,0.0001265243,0.00009010401,0.000002733097,0.0002406774,0.00008881059,0.00001189229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003248889,"about_ca_system_score_gemma":0.00005117386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007904892,"about_ca_topic_score_gemma":0.000007279306,"domain_scores_codex":[0.9987845,0.00008278547,0.000285041,0.0004119129,0.0002178414,0.0002179563],"domain_scores_gemma":[0.9992202,0.000006169956,0.0001070553,0.0005118092,0.00006566036,0.0000891064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005817441,0.00006248914,0.000543625,0.00001388178,0.00004071009,0.000007426348,0.0002352291,0.0007846787,0.9955245,0.00003578259,0.001750042,0.0009434339],"study_design_scores_gemma":[0.003292085,0.001437778,0.03691703,0.000008462006,0.0002060582,0.0001750895,0.005552048,0.0004205573,0.1709662,0.001212667,0.7788442,0.0009677965],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9890947,0.003477114,0.005228287,0.0009041563,0.0002520159,0.0002705804,0.0002064826,0.00002236266,0.0005442698],"genre_scores_gemma":[0.9943554,0.0001633747,0.0005360697,0.0009181224,0.0004262039,0.00004622293,0.001790936,0.00002493133,0.001738692],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8245584,"threshold_uncertainty_score":0.5034814,"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."}}