{"id":"W2624426054","doi":"10.1093/bioinformatics/btx372","title":"Comparison of pre-processing methods for Infinium HumanMethylation450 BeadChip array","year":2017,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network; University of Toronto; Ontario Institute for Cancer Research","funders":"Canadian Institutes of Health Research; Prostate Cancer Canada; Princess Margaret Cancer Foundation; Movember Foundation; Terry Fox Research Institute; Ontario Institute for Cancer Research","keywords":"Replicate; Computer science; Sample (material); DNA methylation; Computational biology; Data mining; Biology; Genetics; Statistics; Mathematics; Gene","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":[0.0005936259,0.000126686,0.0002167203,0.0000458308,0.0003251735,0.00008395207,0.0002994707,0.0001627627,0.000003789553],"category_scores_gemma":[0.0004939213,0.0001192235,0.00009767173,0.00002674541,0.0001043841,0.00001683904,0.00007501732,0.00005592853,0.000001520322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007233054,"about_ca_system_score_gemma":0.00006339068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004953426,"about_ca_topic_score_gemma":0.000006327746,"domain_scores_codex":[0.9990729,0.00002821017,0.0005053772,0.0001216913,0.0001041918,0.0001676073],"domain_scores_gemma":[0.9984729,0.00003650906,0.0006978243,0.0005346895,0.000204912,0.00005315966],"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":[0.00008332577,0.00007356,0.01425628,0.0003657885,0.00005023601,1.741472e-8,0.001155342,0.000411226,0.6421399,0.0003294389,0.0001868482,0.3409481],"study_design_scores_gemma":[0.0004477635,0.0002741396,0.01329584,0.00002554175,0.00003416153,2.491727e-7,0.00014827,0.01418734,0.9367608,0.0007189791,0.03392444,0.0001824098],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1665928,0.0009553137,0.8275364,0.00004476967,0.0001992582,0.0002887273,0.00001966067,0.000009020245,0.004354071],"genre_scores_gemma":[0.5910437,0.00003851391,0.4085579,0.00001672122,0.00009820465,0.00001373879,0.00009337984,0.0000116537,0.0001261988],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4244509,"threshold_uncertainty_score":0.4861792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06380849064092295,"score_gpt":0.4320959425822919,"score_spread":0.3682874519413689,"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."}}