{"id":"W2040626959","doi":"10.1093/nar/gkp573","title":"Importance of randomization in microarray experimental designs with Illumina platforms","year":2009,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Clinical Research Institute","funders":"National Institute of General Medical Sciences; Canadian Institutes of Health Research","keywords":"Normalization (sociology); Biology; DNA microarray; Design of experiments; Computational biology; Confounding; Statistical power; False discovery rate; Randomization; Computer science; Statistical hypothesis testing; Statistics; Bioinformatics; Data mining; Genetics; Mathematics; Gene expression; Gene; Clinical trial","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":[0.0004153766,0.00007698261,0.0001080285,0.0001122269,0.00005035537,0.00001180101,0.0001707504,0.00008294197,0.000043982],"category_scores_gemma":[0.00004289083,0.00006025294,0.00002731011,0.0002604526,0.000111624,0.000007345205,0.00002672401,0.0001035363,0.000003531394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003415112,"about_ca_system_score_gemma":0.00009016814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005484126,"about_ca_topic_score_gemma":0.00001001497,"domain_scores_codex":[0.999056,0.00005097732,0.0001750056,0.0002527414,0.0002573821,0.0002079066],"domain_scores_gemma":[0.9995047,0.000007830025,0.00005054814,0.0002780619,0.0001064809,0.00005232114],"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":[0.001171755,0.0001117002,0.005521126,0.000004994626,0.000004495134,0.00000147042,0.000200966,0.00002768031,0.9899641,0.00009582901,0.0009048709,0.001990987],"study_design_scores_gemma":[0.002508437,0.0006093705,0.01742444,0.00002317277,0.000001167244,0.000003756657,0.0006735573,0.0000764684,0.9771581,0.00007093654,0.001364084,0.00008650729],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958835,0.0003963595,0.0009635239,0.0001108423,0.00001484511,0.0002654885,0.000001633171,0.000004529729,0.002359238],"genre_scores_gemma":[0.9985596,0.00009006415,0.0007825077,0.00005410581,0.0000394418,0.00002934425,0.00003145391,0.00001037608,0.0004030847],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01280602,"threshold_uncertainty_score":0.2457044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04126354493461869,"score_gpt":0.3408971389879385,"score_spread":0.2996335940533198,"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."}}