{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002517324,0.001581578,0.00122295,0.002033244,0.0004908223,0.001213536,0.001961423,0.001484966,0.0700167],"category_scores_gemma":[0.005856245,0.00143361,0.001407408,0.001079341,0.0004858655,0.001325183,0.00266791,0.001921274,0.02687347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007025349,"about_ca_system_score_gemma":0.001177185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002609784,"about_ca_topic_score_gemma":0.003062903,"domain_scores_codex":[0.9989539,0.0001830463,0.00008872584,0.0002973271,0.0003984616,0.00007852941],"domain_scores_gemma":[0.9977805,0.001206032,0.0002792245,0.0003215779,0.0002684687,0.0001441902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004865479,0.0002279661,0.01869146,0.002996201,0.0009909752,0.001028076,0.001092448,0.007920765,0.05449652,0.007556908,0.5648012,0.335332],"study_design_scores_gemma":[0.001519222,0.000641954,0.03327063,0.0006326479,0.0005924502,0.001892585,0.0001928043,0.1038108,0.1218429,0.03288192,0.7020425,0.0006796597],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"software","genre_gemma":"software","genre_scores_codex":[0.006609557,0.0007883278,0.2238841,0.0005041474,0.0002492149,0.0005094722,0.06952677,0.6901214,0.007807074],"genre_scores_gemma":[0.1052717,0.001943229,0.5522418,0.003437561,0.0003156923,0.005272956,0.1597464,0.1350352,0.03673535],"genre_candidate":"software","genre_consensus":"software","teacher_disagreement_score":0.0700167,"threshold_uncertainty_score":0.2342292,"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."}}