{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01496183,0.0003952908,0.0004472214,0.002086079,0.000947156,0.003195819,0.0008328636,0.0007296462,0.007033685],"category_scores_gemma":[0.01951831,0.0002657424,0.0005831812,0.003938582,0.0007642971,0.002533765,0.006532463,0.001921615,0.0009451439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001471143,"about_ca_system_score_gemma":0.005465554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009204672,"about_ca_topic_score_gemma":0.0199203,"domain_scores_codex":[0.9973614,0.001426724,0.0002358604,0.0003983825,0.0002643391,0.0003132845],"domain_scores_gemma":[0.9834015,0.006612106,0.001447486,0.002513577,0.003325879,0.002699466],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009753705,0.0004527752,0.3542323,0.001524716,0.0008319689,0.001044146,0.02144858,0.002279761,0.02205145,0.02332181,0.04310311,0.5287341],"study_design_scores_gemma":[0.0002372207,0.0004186414,0.4956859,0.002814038,0.00109278,0.00188141,0.01933497,0.002967439,0.008443729,0.06932078,0.3976431,0.0001599764],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6376525,0.02818404,0.06049035,0.159882,0.00231381,0.000662722,0.01562772,0.001033848,0.09415298],"genre_scores_gemma":[0.841015,0.01302036,0.09817621,0.03233642,0.001674428,0.000590715,0.007578691,0.000417099,0.005191101],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01496183,"threshold_uncertainty_score":0.07912666,"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."}}