{"id":"W2028245735","doi":"10.1186/1471-2105-9-305","title":"MAID : An effect size based model for microarray data integration across laboratories and platforms","year":2008,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; Toronto Western Hospital; Canada Research Chairs; University of Toronto","funders":"Canadian Institutes of Health Research; Ontario Genomics; Ontario Genomics Institute; Genome Canada","keywords":"Replicate; DNA microarray; Sample size determination; Data mining; Computational biology; Computer science; Statistical power; Gene expression profiling; Permutation (music); Gene chip analysis; Microarray; False discovery rate; Microarray analysis techniques; Gene expression; Biology; Gene; Statistics; Mathematics; Genetics","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":[0.0002494266,0.0001372433,0.0001142133,0.00001805538,0.0001943303,0.00004799012,0.0002304046,0.0001449878,0.000001825858],"category_scores_gemma":[0.0001906936,0.0001063163,0.00002694392,0.00006788897,0.00008229496,0.00004544554,0.00007927888,0.00004842802,0.000001715235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009832796,"about_ca_system_score_gemma":0.0001545666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001693261,"about_ca_topic_score_gemma":0.00006139107,"domain_scores_codex":[0.9993002,0.00001097319,0.0002289896,0.0001953975,0.00009572232,0.0001687072],"domain_scores_gemma":[0.999077,0.0000263132,0.000112328,0.0006091097,0.00009708288,0.00007817867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002112388,0.0002460146,0.01316771,0.001039664,0.00007549374,6.10532e-7,0.0047981,0.005814542,0.9052954,0.0004368815,0.0379677,0.02904553],"study_design_scores_gemma":[0.00113747,0.0002666635,0.0007949348,0.00001464203,0.00001170214,0.000005724806,0.000354976,0.8564888,0.1361234,0.0000424647,0.004563683,0.0001954966],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3885248,0.00009335671,0.6104977,0.0000249109,0.00008457081,0.0003155088,0.0003752242,0.00002025912,0.0000636666],"genre_scores_gemma":[0.8279622,0.00008702576,0.1687221,0.0003542496,0.00009821588,0.00006695647,0.002432565,0.00001971617,0.0002570137],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8506743,"threshold_uncertainty_score":0.4335452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05440643697646198,"score_gpt":0.3195363113182625,"score_spread":0.2651298743418005,"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."}}