{"id":"W4409574103","doi":"10.1038/s41746-025-01603-4","title":"Genomic language models could transform medicine but not yet","year":2025,"lang":"en","type":"article","venue":"npj Digital Medicine","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Genomic medicine; Computational biology; Computer science; Natural language processing; Biology; Genetics; Linguistics; Philosophy","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003602193,0.0002583601,0.0003640935,0.0001748569,0.00007710999,0.00002977133,0.0004582513,0.0002491235,0.0001054889],"category_scores_gemma":[0.0005375853,0.0001819972,0.00008443962,0.000201171,0.0007744066,0.00001386313,0.0001443298,0.0002140682,0.00003842912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003731818,"about_ca_system_score_gemma":0.0001614723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007486239,"about_ca_topic_score_gemma":0.00003143954,"domain_scores_codex":[0.9980825,0.00001850502,0.0005356012,0.0003528977,0.0005255954,0.0004849471],"domain_scores_gemma":[0.9989635,0.00006253785,0.00006680274,0.0004726196,0.0001464281,0.0002880485],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005968106,0.0001909737,0.0002521259,0.0005202194,0.0003136922,0.00004086315,0.001428494,0.00002972438,0.5879577,0.002052719,0.1180073,0.2886093],"study_design_scores_gemma":[0.01413658,0.006209526,0.003147782,0.001186921,0.0002582267,0.00009709333,0.01046731,0.005922436,0.1880297,0.008591519,0.7605745,0.001378486],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5201331,0.005176991,0.02040625,0.02741306,0.001295913,0.001024639,0.0002905373,0.00009538911,0.4241641],"genre_scores_gemma":[0.9789221,0.001337675,0.0001176006,0.003070311,0.000531755,0.00001883276,0.0005894973,0.00002152659,0.0153907],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6425671,"threshold_uncertainty_score":0.742163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01782239376352582,"score_gpt":0.2972402323870185,"score_spread":0.2794178386234927,"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."}}