{"id":"W2591367881","doi":"10.1038/nmeth.4184","title":"Detecting DNA cytosine methylation using nanopore sequencing","year":2017,"lang":"en","type":"article","venue":"Nature Methods","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1257,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research","funders":"","keywords":"Minion; Nanopore sequencing; Nanopore; 5-Methylcytosine; DNA sequencer; DNA methylation; Cytosine; DNA; Computational biology; DNA sequencing; Hidden Markov model; Sequencing by ligation; genomic DNA; Human genome; Computer science; Biology; Genetics; Genome; Genomic library; Nanotechnology; Gene; Base sequence; Materials science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003772455,0.0004562378,0.000290873,0.0003658825,0.0003065846,0.0004748235,0.0005904014,0.0009586653,0.00152755],"category_scores_gemma":[0.0007270714,0.0003448573,0.0002557059,0.000196727,0.00030024,0.0005923677,0.000562161,0.0008306653,0.0008414917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002769294,"about_ca_system_score_gemma":0.0002613505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005513182,"about_ca_topic_score_gemma":0.002012552,"domain_scores_codex":[0.9995758,0.00006757599,0.00001782872,0.0001503704,0.0001425363,0.00004590999],"domain_scores_gemma":[0.9995529,0.0001959225,0.00005680379,0.00007473442,0.0000681598,0.00005146002],"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":[0.00003827366,0.00002212536,0.0007841766,0.00004621773,0.0000231289,0.00002553201,0.00002311249,0.0002933641,0.9866049,0.0006702005,0.0002667803,0.01120224],"study_design_scores_gemma":[0.000005846641,0.00008642345,0.002240819,0.00000948231,0.00002065237,0.0001226243,0.00002498587,0.01034356,0.9814268,0.00125439,0.004444655,0.00001967707],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5343905,0.003760891,0.441092,0.0008677223,0.0004687691,0.0001780442,0.002507549,0.003086819,0.01364763],"genre_scores_gemma":[0.7364927,0.001672328,0.2475213,0.0008714618,0.00007902896,0.0002309925,0.001558201,0.000155593,0.01141851],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.00152755,"threshold_uncertainty_score":0.005110145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04533398034043244,"score_gpt":0.41577286745941,"score_spread":0.3704388871189775,"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."}}