{"id":"W2802491606","doi":"10.1093/nar/gky362","title":"Inferring and modeling inheritance of differentially methylated changes across multiple generations","year":2018,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Wilfrid Laurier University; Université Laval","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies; Ministère de l'Économie, de la Science et de l'Innovation - Québec; Compute Canada; Canadian Institutes of Health Research; Université Laval","keywords":"Biology; DNA methylation; Methylation; Genetics; Computational biology; Randomness; Inheritance (genetic algorithm); Gene; Statistics; 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":[0.00225529,0.0005204994,0.0005523151,0.0009035458,0.000397286,0.0005972952,0.00106561,0.0009790652,0.001159557],"category_scores_gemma":[0.005635524,0.0004714682,0.001121653,0.0006252308,0.0005646045,0.0004911901,0.0005976774,0.0007439173,0.0001278583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001048646,"about_ca_system_score_gemma":0.001100834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01642277,"about_ca_topic_score_gemma":0.01672081,"domain_scores_codex":[0.9994134,0.0001922424,0.00002742631,0.0002601268,0.00005207782,0.00005482425],"domain_scores_gemma":[0.9963055,0.003075264,0.0002758111,0.0001651147,0.0001074091,0.00007105838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001400211,0.0000663294,0.05059271,0.00007828508,0.0001583213,0.0002125839,0.0001354469,0.9194291,0.005349254,0.006631606,0.0003172536,0.01688908],"study_design_scores_gemma":[0.00001168512,0.00002011052,0.003225349,0.000004069155,0.00002601428,0.0000344939,0.000009533982,0.9917666,0.001085437,0.003507217,0.0003026288,0.000006771207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6050439,0.0002780427,0.3917339,0.0001926382,0.0000193332,0.00007423991,0.001498967,0.000517157,0.0006416984],"genre_scores_gemma":[0.8647888,0.0002179522,0.130663,0.00008343434,0.00001917892,0.0002308853,0.002243751,0.00008690233,0.001666202],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01642277,"threshold_uncertainty_score":0.03265435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09369864350801327,"score_gpt":0.3877036534288338,"score_spread":0.2940050099208205,"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."}}