{"id":"W2790370540","doi":"10.3389/fgene.2018.00083","title":"Adjusting for Batch Effects in DNA Methylation Microarray Data, a Lesson Learned","year":2018,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":130,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Children's Hospital; University of British Columbia","funders":"Canadian Institutes of Health Research; BC Children's Hospital","keywords":"Computer science; DNA methylation; Confounding; Sample (material); Sample size determination; Process (computing); Computational biology; Data mining; Biology; Statistics; Genetics; Mathematics; Gene; Chemistry; Chromatography","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":[0.05842486,0.002071431,0.002280299,0.002301621,0.001360622,0.004005854,0.004694336,0.002956735,0.003796825],"category_scores_gemma":[0.1703119,0.001116234,0.003469612,0.001798965,0.005380034,0.00738571,0.002771802,0.01373853,0.003020094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001751552,"about_ca_system_score_gemma":0.00391116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008221182,"about_ca_topic_score_gemma":0.01133243,"domain_scores_codex":[0.9701197,0.01858906,0.001565326,0.004305148,0.0049365,0.0004843571],"domain_scores_gemma":[0.8677833,0.08766684,0.004317833,0.02165581,0.01650546,0.002070837],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005807832,0.0002761791,0.02955355,0.002883756,0.002014659,0.001010252,0.004833875,0.008857809,0.02062142,0.04990743,0.219584,0.6598763],"study_design_scores_gemma":[0.0004485996,0.001176293,0.0377092,0.002184505,0.0009642799,0.003159187,0.001989323,0.04431991,0.04557111,0.4224685,0.4389209,0.001088318],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01105922,0.01278094,0.8439544,0.1144924,0.009192258,0.0002530437,0.0008394683,0.004525002,0.002903264],"genre_scores_gemma":[0.06646168,0.008520346,0.8742109,0.03529852,0.00611661,0.0004772335,0.0007621857,0.003168288,0.004984215],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05842486,"threshold_uncertainty_score":0.3089839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03346328224768329,"score_gpt":0.3161218921411407,"score_spread":0.2826586098934574,"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."}}