{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007046663,0.0001559333,0.0001638609,0.0006953756,0.00008666049,0.00007841368,0.0004673922,0.0002054722,0.000008234348],"category_scores_gemma":[0.0008364866,0.0001567442,0.00005523632,0.001251169,0.00005595884,0.00001190598,0.0003833965,0.00005763258,0.000004844934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003988539,"about_ca_system_score_gemma":0.0000990266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003316271,"about_ca_topic_score_gemma":0.0001818571,"domain_scores_codex":[0.9986068,0.00004570492,0.0003253422,0.0005383407,0.0001811699,0.00030265],"domain_scores_gemma":[0.9988692,0.00005188533,0.0001247915,0.0006761801,0.0002038967,0.00007406842],"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":[0.00004261613,0.00003259233,0.002941917,0.00003002771,0.00003119628,7.243372e-7,0.00005424695,0.00002230203,0.9769073,0.0000154164,0.0002143145,0.01970732],"study_design_scores_gemma":[0.001196648,0.0002987135,0.006346439,0.00002718641,0.00005889534,0.000001233436,0.00004295969,0.3556857,0.6327533,0.001073858,0.002043276,0.0004717688],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.736883,0.001815103,0.260593,0.0000272652,0.0001629307,0.0002291896,0.0001500176,0.00001272739,0.0001267644],"genre_scores_gemma":[0.9406953,0.0003799264,0.05694854,0.00004544633,0.0002522661,0.00001341028,0.001264154,0.0000323314,0.0003686182],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3556634,"threshold_uncertainty_score":0.6391842,"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."}}