{"id":"W4405828636","doi":"10.17615/bg9g-qs46","title":"Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation","year":2024,"lang":"en","type":"article","venue":"MDC Repository (Max-Delbrueck-Center for Molecular Medicine)","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Shionogi; Novo Nordisk; Sanofi; Kowa Company; Wellcome Trust; Respicardia; Regeneron Pharmaceuticals; Yale University; McMaster University; Amgen; National Heart, Lung, and Blood Institute; Pfizer; AstraZeneca; Eli Lilly and Company; GlaxoSmithKline","keywords":"Translation (biology); Type 2 diabetes; Computational biology; Evolutionary biology; Genetics; Biology; Genealogy; Data science; Diabetes mellitus; Computer science; History; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002740751,0.0002053349,0.0002811411,0.0001072604,0.0001219359,0.0000358939,0.0002478612,0.0001432648,0.000002435434],"category_scores_gemma":[0.000112526,0.0001363865,0.0001449883,0.0001315039,0.0003325296,0.00001525388,0.000082787,0.00009213485,4.233326e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001351603,"about_ca_system_score_gemma":0.00007393279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002985156,"about_ca_topic_score_gemma":0.00002012539,"domain_scores_codex":[0.9983402,0.00007222277,0.0005941227,0.0003558812,0.0003691808,0.0002684051],"domain_scores_gemma":[0.9990622,0.00006535561,0.0001368939,0.0004052106,0.0002289449,0.0001013288],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003052447,0.0006447427,0.0123712,0.001031151,0.0006416481,0.00001306447,0.003100487,0.0001213913,0.9751074,0.0001053386,0.001741955,0.004816425],"study_design_scores_gemma":[0.02348616,0.03165058,0.1166847,0.002026033,0.002825485,0.0001065825,0.01602002,0.04017292,0.6885445,0.0007919353,0.07559747,0.00209361],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9753386,0.005573671,0.0164062,0.0002973958,0.0009122054,0.001307102,0.0000837872,0.00001133438,0.00006964442],"genre_scores_gemma":[0.9972317,0.000207054,0.001848427,0.00007208907,0.0001727852,0.00006817329,0.0001676453,0.00002908669,0.0002030466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2865629,"threshold_uncertainty_score":0.5561679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03850785606919284,"score_gpt":0.3146149957806231,"score_spread":0.2761071397114303,"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."}}