{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01400287,0.001622082,0.001060294,0.001703494,0.000805411,0.001889894,0.001723844,0.001072624,0.005871703],"category_scores_gemma":[0.03204897,0.0008196637,0.00200547,0.001651788,0.000500703,0.0009202325,0.001459913,0.001507311,0.002664552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009965497,"about_ca_system_score_gemma":0.001326807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003233442,"about_ca_topic_score_gemma":0.005306453,"domain_scores_codex":[0.9913086,0.003050027,0.0006940346,0.001912386,0.002701341,0.0003335921],"domain_scores_gemma":[0.9845766,0.00916987,0.0006195154,0.002098768,0.003345544,0.0001896959],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0111368,0.0009459735,0.04761197,0.003642779,0.00247461,0.000287016,0.0006749604,0.06859248,0.293385,0.004165532,0.02555321,0.5415297],"study_design_scores_gemma":[0.0004652414,0.002809292,0.1072549,0.0003423768,0.0013857,0.0008194885,0.0004506772,0.2394133,0.5795209,0.00772858,0.05926872,0.0005409463],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2404734,0.007544765,0.7162632,0.001420058,0.0009085296,0.00160943,0.01307838,0.01132811,0.007374192],"genre_scores_gemma":[0.2750731,0.002197032,0.6883723,0.0006961587,0.0001666176,0.002663855,0.02415932,0.002932878,0.003738835],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01400287,"threshold_uncertainty_score":0.07405514,"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."}}