{"id":"W4379348017","doi":"10.1101/2023.05.31.23290808","title":"Pangenome graphs improve the analysis of rare genetic diseases","year":2023,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Genome; Biology; Genetics; Genomics; Computational biology; Human genome; Reference genome; Personal genomics; DNA sequencing; 1000 Genomes Project; Population; Gene; Single-nucleotide polymorphism; Medicine; Genotype","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":[0.001342587,0.0009147187,0.0005467195,0.004165508,0.0005525529,0.001385188,0.00063286,0.0007144991,0.003824885],"category_scores_gemma":[0.005982091,0.0004287186,0.0008590697,0.001934695,0.0004134777,0.001033512,0.001749715,0.001027675,0.0007151973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004951721,"about_ca_system_score_gemma":0.0004237512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002385386,"about_ca_topic_score_gemma":0.003203275,"domain_scores_codex":[0.9991634,0.0002767764,0.0000533603,0.0003042677,0.000145777,0.00005642688],"domain_scores_gemma":[0.9961128,0.002271401,0.000453552,0.000608596,0.0003463289,0.0002071698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001607819,0.0003468107,0.1711346,0.001745962,0.002254213,0.002135257,0.001146883,0.105619,0.2518251,0.03704992,0.02623562,0.398899],"study_design_scores_gemma":[0.000234853,0.0003296915,0.125202,0.0003630274,0.0009592547,0.003141956,0.0006313875,0.4978004,0.07710459,0.2039883,0.09003057,0.0002139608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.425842,0.004942854,0.5331227,0.001398301,0.0003386722,0.0001417812,0.01651317,0.01114681,0.006553782],"genre_scores_gemma":[0.7568697,0.00143097,0.2222119,0.0005719293,0.0001632143,0.0001334866,0.01439336,0.00218788,0.0020376],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004165508,"threshold_uncertainty_score":0.01279551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01323020988937376,"score_gpt":0.2474314154925458,"score_spread":0.2342012056031721,"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."}}