{"id":"W3112474449","doi":"10.12688/wellcomeopenres.16458.2","title":"MethylDetectR: a software for methylation-based health profiling","year":2021,"lang":"en","type":"preprint","venue":"Wellcome Open Research","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; 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":"DNA methylation; Methylation; Trait; Biology; Quantitative trait locus; Phenotype; Profiling (computer programming); Genetics; Computational biology; Computer science; Gene; Gene expression","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":[0.007358127,0.0003239056,0.0005385933,0.0002483108,0.0004887657,0.0007238194,0.001454971,0.0005906546,0.00005466025],"category_scores_gemma":[0.001623281,0.0003402188,0.0002433156,0.0003820874,0.0001083288,0.000005870697,0.002485479,0.0008343497,0.000008403897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000146915,"about_ca_system_score_gemma":0.003813874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005886349,"about_ca_topic_score_gemma":0.0002961204,"domain_scores_codex":[0.9953477,0.001222379,0.0006075997,0.001395733,0.0006469666,0.00077967],"domain_scores_gemma":[0.9962308,0.0003462954,0.0002670815,0.001400386,0.001492521,0.000262879],"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":[0.001037861,0.000835311,0.003760608,0.00422222,0.0008374414,0.00001313364,0.0003870336,0.02607742,0.8257654,0.0005655276,0.003840714,0.1326573],"study_design_scores_gemma":[0.001126609,0.0007917234,0.0002922309,0.0002686911,0.00002200464,4.67992e-7,0.0001654133,0.003385373,0.9467529,0.002716901,0.04393047,0.0005471773],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3100911,0.05923192,0.6004751,0.00511491,0.001568946,0.01869883,0.001022449,0.00008988768,0.003706852],"genre_scores_gemma":[0.6093509,0.002387803,0.3629273,0.0004640712,0.0009894003,0.003864454,0.01621977,0.0002830208,0.003513385],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2992598,"threshold_uncertainty_score":0.999905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1790752849366111,"score_gpt":0.4530977749758222,"score_spread":0.2740224900392111,"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."}}