{"id":"W4225518108","doi":"10.1186/s13148-022-01268-w","title":"Epigenome-wide contributions to individual differences in childhood phenotypes: a GREML approach","year":2022,"lang":"en","type":"article","venue":"Clinical Epigenetics","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Jewish General Hospital","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; H2020 Marie Skłodowska-Curie Actions; Economic and Social Research Council; Medical Research Council; Horizon 2020; Canadian Institutes of Health Research; ZonMw; Wellcome Trust; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Biotechnology and Biological Sciences Research Council; Erasmus Universiteit Rotterdam","keywords":"Epigenome; Human genetics; Phenotype; Computational biology; Biology; Bioinformatics; Genetics; Medicine; DNA methylation; Gene","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001450741,0.0002397744,0.0003990775,0.0001277067,0.0002725847,0.00004149406,0.0006005106,0.0002102836,0.00006973992],"category_scores_gemma":[0.001296428,0.0002534335,0.0001937361,0.000400723,0.0001181351,0.000002713475,0.0006960023,0.0004337383,0.00001934057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000459967,"about_ca_system_score_gemma":0.0002710968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002836115,"about_ca_topic_score_gemma":0.00005184516,"domain_scores_codex":[0.9969526,0.0005534243,0.0008839623,0.0007376723,0.0003755094,0.000496842],"domain_scores_gemma":[0.9986346,0.0001964917,0.000179472,0.0005840873,0.000117767,0.0002875482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000121873,0.001710905,0.9766815,0.00001148787,0.0001280914,0.000005977852,0.0004152167,0.005255456,0.00322855,0.0005026014,0.0005184176,0.01141994],"study_design_scores_gemma":[0.001318392,0.001563752,0.9585204,0.000006429814,0.00004714885,8.883507e-7,0.000248945,0.0001772794,0.002120072,0.001954105,0.03355265,0.000489907],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9788282,0.00289873,0.01533613,0.0004017777,0.0004502197,0.0006170192,0.0003719888,0.00001907867,0.001076857],"genre_scores_gemma":[0.9940896,0.0005747633,0.003158186,0.000922316,0.0003884623,0.0001775863,0.0004437581,0.00003686804,0.0002084298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03303423,"threshold_uncertainty_score":0.9999918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03759920215336466,"score_gpt":0.3329551804845823,"score_spread":0.2953559783312176,"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."}}