{"id":"W2023746329","doi":"10.1371/journal.pone.0065380","title":"Effects of Sample Size on Differential Gene Expression, Rank Order and Prediction Accuracy of a Gene Signature","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Innovates; University of Calgary; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Alberta Innovates; Alberta Cancer Foundation","keywords":"Sample size determination; Discriminative model; DNA microarray; Gene; Biology; Microarray; Computational biology; Microarray analysis techniques; Gene expression; Rank (graph theory); Genetics; Sample (material); Gene expression profiling; Phenotype; Bioinformatics; Statistics; Mathematics; Computer science; Artificial intelligence","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":[0.00003114723,0.0001075149,0.0001606338,0.00003176329,0.00003303024,0.000007837868,0.00008921858,0.0001515376,0.00008920767],"category_scores_gemma":[0.0005091031,0.00008864433,0.00003469027,0.00006368001,0.00004130143,0.000005289577,0.0000437965,0.0000669366,0.000001491961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004669538,"about_ca_system_score_gemma":0.00002422822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001227892,"about_ca_topic_score_gemma":5.506167e-7,"domain_scores_codex":[0.9992386,0.00005598894,0.0001818345,0.0002370698,0.0001833049,0.000103257],"domain_scores_gemma":[0.9992887,0.00009566107,0.0001462977,0.0002446969,0.0001669391,0.00005768513],"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":[0.0001389487,0.0005503781,0.001039877,0.0001499889,0.00006852238,7.809884e-8,0.00004891303,0.000004279897,0.996516,0.000003834636,0.0007729395,0.0007062571],"study_design_scores_gemma":[0.0009061405,0.0002816562,0.02689184,0.0001021987,0.00004713491,2.609333e-7,0.00001292588,0.00007202023,0.9714729,0.0000630574,0.00007176567,0.00007812615],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970936,0.0005148728,0.001781508,0.00007700318,0.00004709628,0.0004018423,0.00003479806,0.000008438128,0.00004080873],"genre_scores_gemma":[0.995463,0.0004384202,0.003582042,0.0000676949,0.0001249566,0.000101224,0.00007022948,0.00001422408,0.000138167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02585196,"threshold_uncertainty_score":0.3614811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01063623819297326,"score_gpt":0.2137728378606499,"score_spread":0.2031365996676766,"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."}}