{"id":"W4389391230","doi":"10.1038/s41588-023-01608-3","title":"Inferring compound heterozygosity from large-scale exome sequencing data","year":2023,"lang":"en","type":"article","venue":"Nature Genetics","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; McGill Genome Centre; Montreal Heart Institute; University of Ottawa","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; Eisai; National Human Genome Research Institute; GlaxoSmithKline; Biogen; Astellas Pharma; Wellcome Trust; U.S. Department of Health and Human Services","keywords":"Biology; Genetics; Exome sequencing; Exome; Compound heterozygosity; Gene; Computational biology; Loss of heterozygosity; Mendelian inheritance; 1000 Genomes Project; Context (archaeology); Genome; Whole genome sequencing; Genotype; Allele; Mutation; Single-nucleotide polymorphism","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":[0.0001463613,0.0001735629,0.0001442318,0.00004626956,0.0001440795,0.00005901262,0.0006569426,0.0003570483,0.00001710942],"category_scores_gemma":[0.00005189684,0.0001745361,0.00006617048,0.0001373963,0.00003651011,0.000004121021,0.001029972,0.0002355255,0.00004612696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001996245,"about_ca_system_score_gemma":0.00009840746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001998322,"about_ca_topic_score_gemma":0.0001864436,"domain_scores_codex":[0.9987081,0.00003380855,0.0001740663,0.0005698823,0.000171593,0.0003425201],"domain_scores_gemma":[0.9986107,0.00001429366,0.00005844982,0.001125786,0.00006550772,0.0001252144],"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":[0.00009036944,0.0001316654,0.1259734,0.00006954958,0.0003136689,0.0001916923,0.0003442291,0.001838101,0.8310444,0.00002082033,0.03746996,0.002512131],"study_design_scores_gemma":[0.002029575,0.0002127993,0.2619292,0.00006818396,0.0002033118,0.00006018867,0.0008149538,0.0154769,0.1517341,0.001114303,0.5649076,0.001448863],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929822,0.004085838,0.00038922,0.00007401976,0.0005698222,0.0001165813,0.001469974,0.00004629141,0.0002660695],"genre_scores_gemma":[0.9896805,0.000709418,0.001744046,0.0005700146,0.0006823874,0.000003986069,0.006408958,0.00003588016,0.0001647918],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6793103,"threshold_uncertainty_score":0.7117377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0244610955105098,"score_gpt":0.285495727216693,"score_spread":0.2610346317061832,"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."}}