{"id":"W2889050153","doi":"10.1111/biom.12965","title":"A Hidden Markov Model for Identifying Differentially Methylated Sites in Bisulfite Sequencing Data","year":2018,"lang":"en","type":"article","venue":"Biometrics","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; HEC Montréal; Jewish General Hospital","funders":"Division of Materials Research; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Ludmer Centre for Neuroinformatics and Mental Health","keywords":"Hidden Markov model; DNA methylation; Bisulfite sequencing; Identification (biology); Autocorrelation; Computer science; CpG site; Computational biology; Differentially methylated regions; Bisulfite; Methylation; Markov chain; Selection (genetic algorithm); Biology; Data mining; Genetics; Artificial intelligence; Statistics; Machine learning; Mathematics; DNA; Gene; Ecology","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":[0.004188078,0.001071071,0.001523447,0.001284599,0.0007972012,0.001112117,0.002587801,0.001981496,0.002925114],"category_scores_gemma":[0.007907464,0.0009544558,0.001801472,0.001156432,0.0009841181,0.001664708,0.001162671,0.003078918,0.00080616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001614061,"about_ca_system_score_gemma":0.002077475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02323399,"about_ca_topic_score_gemma":0.02271357,"domain_scores_codex":[0.9987535,0.0004576153,0.00008145168,0.0003863238,0.0001846043,0.000136434],"domain_scores_gemma":[0.9944056,0.004665563,0.0003225532,0.0001630388,0.0003484515,0.00009486945],"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":[0.0004264739,0.0001081734,0.007574683,0.000217967,0.000209309,0.0003211396,0.0002651011,0.9001154,0.003382806,0.03462308,0.001955424,0.05080058],"study_design_scores_gemma":[0.00001522888,0.00001802627,0.0003452704,0.00001095124,0.00001924632,0.00002122422,0.000007177547,0.9902839,0.0004014769,0.008499736,0.0003639597,0.00001368786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02702307,0.0006009032,0.9693086,0.0004453277,0.00008388329,0.0001096876,0.0009034638,0.0009164835,0.000608573],"genre_scores_gemma":[0.5885426,0.001471098,0.3963159,0.0006524865,0.0001960258,0.001230117,0.004356386,0.0002720114,0.006963351],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02323399,"threshold_uncertainty_score":0.04619747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1418959052330344,"score_gpt":0.3623946788056521,"score_spread":0.2204987735726177,"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."}}