{"id":"W3134713371","doi":"10.2217/epi-2020-0271","title":"Comparative Epigenome-Wide Analysis Highlights Placenta-Specific Differentially Methylated Regions","year":2021,"lang":"en","type":"article","venue":"Epigenomics","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"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":"Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research; Fonds de Recherche du Québec - Santé; Compute Canada; American Diabetes Association","keywords":"dNaM; Biology; Placenta; Epigenome; DNA methylation; Differentially methylated regions; Cord blood; Genome; Epigenetics; Fetus; Genomic imprinting; Gene; Computational biology; Genetics; Bioinformatics; Pregnancy; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002092665,0.000294593,0.0004874168,0.0001591846,0.0002510063,0.00009171605,0.0002582789,0.0002328323,0.0001690081],"category_scores_gemma":[0.00004133279,0.0003055076,0.0003803388,0.0005971685,0.00008961416,0.000005602067,0.0001676115,0.0001462935,0.00005731134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006671247,"about_ca_system_score_gemma":0.000151343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002266929,"about_ca_topic_score_gemma":0.000462479,"domain_scores_codex":[0.9979503,0.0002490716,0.0004799957,0.0007272571,0.0001943859,0.0003990634],"domain_scores_gemma":[0.9984624,0.00007038037,0.0002016738,0.0007886838,0.0002835343,0.000193298],"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":[0.0001237011,0.000262016,0.01072654,0.00001198699,0.00207068,0.00002662814,0.0005933201,0.004886841,0.9782597,0.00174861,0.0009052058,0.0003848033],"study_design_scores_gemma":[0.0005694062,0.0001040684,0.03966405,0.000005443162,0.0003924656,0.000001097239,0.0001562617,0.0001617898,0.7689621,0.0008588512,0.188632,0.0004924516],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9156041,0.01059989,0.07031279,0.0002839235,0.0003393214,0.0001909032,0.00007778095,0.00002761164,0.002563724],"genre_scores_gemma":[0.9844359,0.006835972,0.003753505,0.0001089884,0.0002515938,0.0000169848,0.002079015,0.00003692922,0.002481133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2092975,"threshold_uncertainty_score":0.9999397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02551062519440742,"score_gpt":0.2680971919412832,"score_spread":0.2425865667468758,"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."}}