{"id":"W4408157009","doi":"10.1038/s41591-025-03544-7","title":"Advancing clinical genomics with Middle Eastern and South Asian pangenomes","year":2025,"lang":"en","type":"article","venue":"Nature Medicine","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Genome Canada","funders":"","keywords":"Middle East; Equity (law); Precision medicine; Genomics; South asia; East Asia; Global health; Geography; Political science; Medicine; History; Ancient history; Biology; China; Genetics; Genome; Pathology; Public health; Gene; Archaeology","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":[0.0004492796,0.0001592745,0.0002519941,0.00009255666,0.00007168246,0.00001524415,0.0002075914,0.0004000578,0.00001413006],"category_scores_gemma":[0.0005316852,0.0001028828,0.00003941616,0.0001294443,0.0004696618,0.000002607608,0.0001281891,0.0004528514,0.000004840881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001387664,"about_ca_system_score_gemma":0.0001618708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006088724,"about_ca_topic_score_gemma":0.00006988177,"domain_scores_codex":[0.9987715,0.00004315928,0.0003198151,0.0003096659,0.0002422108,0.0003136325],"domain_scores_gemma":[0.9992286,0.00003581504,0.00007281298,0.0003155636,0.0001309704,0.0002162341],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001737175,0.000290609,0.2755093,0.00125132,0.0009414057,0.00006489772,0.002771433,0.000005726286,0.06242293,0.0004439501,0.03020925,0.624352],"study_design_scores_gemma":[0.00606177,0.003380956,0.06532384,0.0005219037,0.000180836,0.00004073083,0.006585998,0.0004860583,0.01220144,0.0002926389,0.9043908,0.0005330659],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9563667,0.01376719,0.004862941,0.007946798,0.0007198198,0.0004549089,0.0000202669,0.00002309129,0.01583827],"genre_scores_gemma":[0.9886456,0.001611374,0.002611105,0.003088051,0.0008160407,0.000006498146,0.00009701902,0.00001515832,0.003109207],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8741815,"threshold_uncertainty_score":0.4195439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0134071616483031,"score_gpt":0.3159198185035413,"score_spread":0.3025126568552383,"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."}}