{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009053047,0.0001307167,0.0002236651,0.0001496798,0.00002563139,0.00001264923,0.0000837179,0.0001454257,0.000006936423],"category_scores_gemma":[0.00001588315,0.0001107454,0.00004164401,0.0001855413,0.00008026323,0.00000319502,0.0000464089,0.00007558923,5.168484e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005212855,"about_ca_system_score_gemma":0.00001191787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001927872,"about_ca_topic_score_gemma":0.000003074575,"domain_scores_codex":[0.9989354,0.0001824456,0.0003039916,0.0003154673,0.0001254603,0.0001372612],"domain_scores_gemma":[0.9994858,0.00003446899,0.0001111918,0.0002651282,0.00005098163,0.0000524572],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002441748,0.0001241773,0.01670739,0.00007346049,0.00001841598,0.000001278498,0.00008221863,0.0000848055,0.6589563,0.00002437569,0.000008343846,0.3238948],"study_design_scores_gemma":[0.004672474,0.01175651,0.1798654,0.00039034,0.00008117292,0.000002597195,0.00005210577,0.03161741,0.7547709,0.001018648,0.01538194,0.000390441],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.868526,0.0008291337,0.1295962,0.00004940684,0.0002663286,0.000393321,0.000003457595,0.00001006333,0.0003260971],"genre_scores_gemma":[0.8321978,0.00007049439,0.1671845,0.00004863841,0.0002020155,0.00001286063,0.0002196562,0.00001292711,0.00005108773],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3235044,"threshold_uncertainty_score":0.4516067,"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."}}