{"id":"W4416599612","doi":"10.1038/s41588-025-02400-1","title":"Proteome-wide model for human disease genetics","year":2025,"lang":"en","type":"article","venue":"Nature Genetics","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Cancer Institute; National Institutes of Health; National Science Foundation; Generalitat de Catalunya; Agencia Estatal de Investigación; Invitae; Ministerio de Ciencia e Innovación; Centres de Recerca de Catalunya","keywords":"Disease; Missense mutation; Limiting; Population; Human disease; Human genetic variation; Generative grammar; Medical genetics; Human genetics","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":[0.001994797,0.0008675994,0.001060824,0.001032299,0.0004181287,0.001276289,0.001819244,0.001669505,0.005371037],"category_scores_gemma":[0.005873284,0.0005143571,0.001348305,0.001159842,0.0009850815,0.001153199,0.001282116,0.0020775,0.00149546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001247411,"about_ca_system_score_gemma":0.001175575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00908538,"about_ca_topic_score_gemma":0.009102203,"domain_scores_codex":[0.9994454,0.0002241029,0.00001826437,0.0001962403,0.00006680752,0.00004919636],"domain_scores_gemma":[0.9989057,0.0007205565,0.00008488862,0.0001053793,0.0001180179,0.00006549145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001582184,0.00005397549,0.007010017,0.0001021929,0.0002305953,0.0003305293,0.0001055015,0.8849108,0.001565508,0.06559992,0.008157257,0.03177553],"study_design_scores_gemma":[0.00002303368,0.00001280304,0.0008172378,0.00001154019,0.0000225039,0.00008434495,0.000007756467,0.9362056,0.0001444833,0.06079872,0.001859537,0.00001241129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06097411,0.001993241,0.919498,0.002766798,0.0002197903,0.0001093123,0.006678398,0.002517451,0.005242882],"genre_scores_gemma":[0.8110815,0.002169272,0.1584857,0.001896681,0.0003375582,0.0006137806,0.01004819,0.000636538,0.01473082],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00908538,"threshold_uncertainty_score":0.01806504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007377347058354692,"score_gpt":0.2790728267176209,"score_spread":0.2716954796592662,"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."}}