{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002947499,0.0001528907,0.000291841,0.001100061,0.0001873689,0.0003087172,0.000168974,0.0002089516,0.002953655],"category_scores_gemma":[0.0005710873,0.0001003319,0.0003201487,0.001026101,0.000153725,0.0001101923,0.0003082085,0.0002356525,0.0002182175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001456258,"about_ca_system_score_gemma":0.0001555697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00107626,"about_ca_topic_score_gemma":0.001953689,"domain_scores_codex":[0.9998068,0.00002965666,0.000009747809,0.00008833399,0.00003133921,0.0000341477],"domain_scores_gemma":[0.9996768,0.0001134308,0.0001165667,0.00002633354,0.00003077053,0.00003614244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00139189,0.00003648164,0.178216,0.0004277134,0.0008811408,0.0004530802,0.0002816695,0.0004970753,0.7833156,0.0004099811,0.0004415711,0.03364778],"study_design_scores_gemma":[0.00001102414,0.0001467077,0.9664907,0.00001588708,0.0002589709,0.00100116,0.0001280499,0.0005810757,0.0284699,0.0002624504,0.002625731,0.000008442121],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9851955,0.003882692,0.004651007,0.0001003443,0.00001013795,0.0000120943,0.00475958,0.00007630516,0.001312284],"genre_scores_gemma":[0.9934403,0.0008134986,0.002225122,0.0000592221,0.000009741951,0.00001908666,0.002656612,0.00001793104,0.0007585245],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002953655,"threshold_uncertainty_score":0.00988096,"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."}}