{"id":"W2916934838","doi":"10.1016/j.cmpb.2019.02.010","title":"Human age prediction based on DNA methylation of non-blood tissues","year":2019,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"National Natural Science Foundation of China","keywords":"DNA methylation; CpG site; Methylation; Regression; Epigenetics; Correlation; Linear regression; Regression analysis; Biology; Computational biology; Statistics; Genetics; DNA; Mathematics; Gene expression; Gene","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":[0.0003823531,0.0003565022,0.000337785,0.0009683002,0.0001322879,0.0003414234,0.0001944258,0.0003101785,0.001664775],"category_scores_gemma":[0.001585192,0.0001281779,0.0003932182,0.0003720002,0.00008794729,0.0002902129,0.0002321353,0.0002480269,0.0008955103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001124949,"about_ca_system_score_gemma":0.0001768225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001699761,"about_ca_topic_score_gemma":0.001737357,"domain_scores_codex":[0.9998823,0.00002377965,0.000008950957,0.00005000305,0.00001881355,0.00001607237],"domain_scores_gemma":[0.9993821,0.0003151968,0.00007646278,0.00004525645,0.000131972,0.0000488474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002044181,0.00006157642,0.6233491,0.0001682981,0.000204796,0.0006098793,0.00014599,0.02234212,0.02977328,0.0009668157,0.003451702,0.3168822],"study_design_scores_gemma":[0.00007161548,0.0005235981,0.4810986,0.00008314183,0.0005672845,0.003394306,0.0001731219,0.4626561,0.03556814,0.006982974,0.008809633,0.00007141953],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8076601,0.002489912,0.1795484,0.0001744233,0.00009676154,0.00005270405,0.005165497,0.001510159,0.003302085],"genre_scores_gemma":[0.9719906,0.0004696997,0.02420232,0.00003819522,0.00003965928,0.00002321547,0.001790988,0.00004565269,0.001399551],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001699761,"threshold_uncertainty_score":0.005569279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02626853970150107,"score_gpt":0.3491001443020759,"score_spread":0.3228316046005748,"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."}}