{"id":"W4409187187","doi":"10.1038/s41467-025-58465-3","title":"The Estonian Biobank’s journey from biobanking to personalized medicine","year":2025,"lang":"en","type":"review","venue":"Nature Communications","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Research Executive Agency; HORIZON EUROPE Framework Programme; Eesti Teadusagentuur; Tartu Ülikool; European Commission","keywords":"Biobank; Personalized medicine; Estonian; Precision medicine; Genomics; Medicine; Translational medicine; Data science; Bioinformatics; Computer science; Genetics; Biology; Pathology; Genome","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":[0.008782558,0.0006519131,0.001215591,0.003018307,0.0008915297,0.004266293,0.001302842,0.001823746,0.003712201],"category_scores_gemma":[0.006095222,0.0003034203,0.0006422789,0.004761453,0.001333178,0.003292862,0.002551569,0.002241095,0.00152864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003931564,"about_ca_system_score_gemma":0.0101157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009421459,"about_ca_topic_score_gemma":0.008906917,"domain_scores_codex":[0.9978833,0.0007939973,0.0003443698,0.0002232105,0.0005805701,0.0001745274],"domain_scores_gemma":[0.9937651,0.003334486,0.0007050105,0.0002291984,0.001437785,0.0005283889],"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":[0.0001738166,0.00005402214,0.001991567,0.01340019,0.0001617054,0.0009069502,0.0007318846,0.0004126651,0.0005807009,0.03221054,0.08616532,0.8632107],"study_design_scores_gemma":[0.00001018112,0.00003696807,0.003598709,0.006286515,0.00005624881,0.0007162367,0.0002425287,0.00005894083,0.000193875,0.002462594,0.9863167,0.00002044921],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000832235,0.9834809,0.0008933855,0.00991337,0.0008659175,0.00001301152,0.0002879695,0.00003498838,0.003678169],"genre_scores_gemma":[0.007070846,0.982957,0.002105158,0.004031053,0.0007877665,0.00003930959,0.0006379412,0.00001800438,0.002352969],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.009421459,"threshold_uncertainty_score":0.0464471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0501004979382797,"score_gpt":0.406896144851087,"score_spread":0.3567956469128074,"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."}}