{"id":"W3134842602","doi":"10.2217/epi-2020-0344","title":"Detecting Differentially Methylated Regions with Multiple Distinct Associations","year":2021,"lang":"en","type":"article","venue":"Epigenomics","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean; Centre Hospitalier Universitaire de Sherbrooke; Université de Sherbrooke","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Nursing Research; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; National Institute of Environmental Health Sciences; Canadian Institutes of Health Research; Fonds de Recherche du Québec - Santé; Diabète Québec; National Institutes of Health; National Institute of General Medical Sciences; American Diabetes Association","keywords":"Biology; CpG site; Type I and type II errors; Statistical power; Computational biology; Differentially methylated regions; DNA methylation; Statistics; Bioinformatics; Genetics; Computer science; Evolutionary biology; Gene; Mathematics; Gene expression","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":[0.02012118,0.001171483,0.001027088,0.001565144,0.0006809391,0.001111437,0.001624065,0.001326249,0.003153598],"category_scores_gemma":[0.04956243,0.0005134774,0.00225989,0.0008999402,0.0009459151,0.0008917914,0.001499941,0.001402154,0.0003982533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008372642,"about_ca_system_score_gemma":0.001173089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002516742,"about_ca_topic_score_gemma":0.0039102,"domain_scores_codex":[0.9930362,0.003917146,0.0003092875,0.001863427,0.0006812346,0.000192607],"domain_scores_gemma":[0.9417806,0.05180484,0.002664744,0.001992452,0.001218027,0.0005394454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004038096,0.0003307225,0.5517005,0.002570454,0.007614069,0.0008389865,0.0005966268,0.1322752,0.04191389,0.007150733,0.002580927,0.2483897],"study_design_scores_gemma":[0.000965712,0.001656228,0.1380484,0.0004737505,0.003384284,0.002599029,0.0003474101,0.7446113,0.07133656,0.02932242,0.006962881,0.0002920443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4185972,0.002982164,0.570986,0.0009210202,0.0001564105,0.0004273659,0.002128237,0.002198921,0.001602777],"genre_scores_gemma":[0.7872769,0.0002799266,0.2098292,0.0003084869,0.00005132414,0.000243865,0.001046663,0.0002128069,0.0007507621],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02012118,"threshold_uncertainty_score":0.1064123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01506899165380337,"score_gpt":0.2430397883676819,"score_spread":0.2279707967138785,"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."}}