{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002733892,0.001222442,0.001483902,0.003594065,0.0004484543,0.001777775,0.001015577,0.001218317,0.001286084],"category_scores_gemma":[0.01047715,0.0007708318,0.001369533,0.002022756,0.0004368705,0.001418074,0.001373479,0.001242493,0.0006161022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000294159,"about_ca_system_score_gemma":0.001033094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002422438,"about_ca_topic_score_gemma":0.005311945,"domain_scores_codex":[0.9987942,0.0003030308,0.0001167611,0.0004694688,0.000199637,0.0001168543],"domain_scores_gemma":[0.9931791,0.005405472,0.0005202769,0.0004081274,0.0002721722,0.000214844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001772842,0.0005590818,0.512534,0.001010409,0.003231814,0.01139083,0.0003990174,0.1191744,0.1337382,0.006910965,0.00629291,0.2029854],"study_design_scores_gemma":[0.0003858804,0.0004171687,0.1811029,0.0001943691,0.001965713,0.009624497,0.0004973022,0.692376,0.02691187,0.07926197,0.007052229,0.0002101194],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6697977,0.001902361,0.3105755,0.0006966356,0.00009593584,0.0001238932,0.01420247,0.00158825,0.001017298],"genre_scores_gemma":[0.888997,0.0008445675,0.09420189,0.0002748179,0.0001225444,0.0000576426,0.01476471,0.0001773488,0.0005595252],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003594065,"threshold_uncertainty_score":0.01445836,"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."}}