{"id":"W4241052313","doi":"10.12688/wellcomeopenres.16458.1","title":"MethylDetectR: a software for methylation-based health profiling","year":2020,"lang":"en","type":"preprint","venue":"Wellcome Open Research","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council; Chief Scientist Office, Scottish Government Health and Social Care Directorate; Institute of Genetics; National Institutes of Health; Centre for Cognitive Ageing and Cognitive Epidemiology; Alzheimer’s Research UK; University of Queensland; University of Edinburgh; Dementias Platform UK; Age UK; Scottish Government; Scottish Funding Council; Wellcome Trust","keywords":"Profiling (computer programming); Software; Methylation; Computational biology; Computer science; Biology; Operating system; Genetics; Gene","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":[0.005627441,0.0003335187,0.0005369609,0.0002139204,0.0004482642,0.0004638763,0.001727866,0.0005258636,0.00003376534],"category_scores_gemma":[0.001755453,0.0003436412,0.0002217031,0.0003678521,0.0001112736,0.000004640306,0.002479722,0.0008989369,0.00002448044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001242696,"about_ca_system_score_gemma":0.002810417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003907685,"about_ca_topic_score_gemma":0.0001021564,"domain_scores_codex":[0.9956385,0.0009893745,0.0006058529,0.001402314,0.0006329361,0.0007310432],"domain_scores_gemma":[0.9970133,0.0003247285,0.0002844149,0.001108353,0.0009260401,0.0003431261],"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":[0.002056663,0.000532959,0.002550923,0.004626889,0.0007705594,0.000008663033,0.0003595542,0.01726409,0.8449581,0.001547433,0.01131609,0.114008],"study_design_scores_gemma":[0.001367713,0.001485366,0.0002256916,0.0001842556,0.00002482478,2.592319e-7,0.00007165004,0.006819274,0.8595222,0.014299,0.1153858,0.0006140542],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06834733,0.03150788,0.8449999,0.01943241,0.001373801,0.02704808,0.002068694,0.0001484769,0.005073441],"genre_scores_gemma":[0.6199723,0.001596114,0.3565178,0.0009357408,0.001523377,0.004194604,0.01275368,0.0003473036,0.002159072],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.551625,"threshold_uncertainty_score":0.9999015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2263201545463757,"score_gpt":0.4593537792161789,"score_spread":0.2330336246698031,"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."}}