{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02426898,0.001352053,0.001324854,0.002681668,0.0008245045,0.001840651,0.003210382,0.002102223,0.004063],"category_scores_gemma":[0.05645457,0.0007692365,0.00373205,0.001916856,0.001339414,0.0008255981,0.002217071,0.0028747,0.0008128068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008733622,"about_ca_system_score_gemma":0.0007520746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0079385,"about_ca_topic_score_gemma":0.005374566,"domain_scores_codex":[0.9866088,0.01061551,0.0003214057,0.001910771,0.0003513288,0.0001921485],"domain_scores_gemma":[0.9505126,0.04225511,0.001342014,0.004667872,0.0009873396,0.0002350648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001856205,0.0003146571,0.3365508,0.0006515229,0.0090412,0.00160655,0.001477341,0.4088607,0.009230103,0.02380327,0.007038058,0.1995696],"study_design_scores_gemma":[0.0001296457,0.0002650999,0.04779213,0.0001208737,0.0007816844,0.0005463996,0.0001735187,0.9116881,0.002328818,0.03043352,0.005665274,0.00007496694],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1497081,0.0008335316,0.8412731,0.0009215675,0.0000724436,0.0001843375,0.003860289,0.002288531,0.0008580944],"genre_scores_gemma":[0.6629101,0.0002728264,0.3252726,0.000608198,0.0001069103,0.0008890716,0.007279663,0.0005615233,0.00209909],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02426898,"threshold_uncertainty_score":0.1283482,"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."}}