{"id":"W4405630466","doi":"10.1186/s13148-024-01784-x","title":"Maximizing insights from longitudinal epigenetic age data: simulations, applications, and practical guidance","year":2024,"lang":"en","type":"article","venue":"Clinical Epigenetics","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Mental Health; Canadian Institutes of Health Research; Engineering and Physical Sciences Research Council; Science Foundation Ireland; National Institute on Aging; UK Research and Innovation","keywords":"dNaM; Epigenetics; DNA methylation; Longitudinal data; Demography; Longitudinal study; Robustness (evolution); Mixed model; Confounding; Medicine; Biology; Bioinformatics; Statistics; Genetics; Internal medicine; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005137676,0.0002806449,0.0003181098,0.00006418489,0.0001650032,0.0002168433,0.0003259628,0.0003991249,0.00002458593],"category_scores_gemma":[0.0007839382,0.0002666342,0.000102418,0.0001831381,0.0003093524,0.00001756462,0.0005892137,0.0003167285,0.00005021537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001478449,"about_ca_system_score_gemma":0.0001970767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000244075,"about_ca_topic_score_gemma":0.0001169781,"domain_scores_codex":[0.9970388,0.0002502606,0.0009021878,0.001265818,0.0002565401,0.0002863322],"domain_scores_gemma":[0.997484,0.0006410792,0.0001396443,0.001363096,0.0001154354,0.0002567003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003420577,0.001455044,0.2257015,0.0004889099,0.001575507,0.0004056095,0.0002398267,0.004945406,0.3355236,0.007371932,0.005248959,0.4167017],"study_design_scores_gemma":[0.0008861202,0.0005572235,0.1040864,0.0001254174,0.000432086,0.00001189581,0.00004209581,0.03486324,0.0233211,0.02364556,0.8110006,0.001028206],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5356113,0.1022468,0.3594315,0.0005410223,0.0007512707,0.000589111,0.000353014,0.00007396191,0.0004020857],"genre_scores_gemma":[0.9450934,0.01362492,0.03784622,0.0001946149,0.001665655,0.00002955366,0.001274625,0.0000618301,0.0002091668],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8057517,"threshold_uncertainty_score":0.9999786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1180823016676553,"score_gpt":0.4275485559060074,"score_spread":0.3094662542383522,"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."}}