{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05295611,0.0005842263,0.001338289,0.0007414278,0.0007700407,0.001322466,0.0005468568,0.0008379531,0.001112644],"category_scores_gemma":[0.08948211,0.000341103,0.001283758,0.0007014915,0.001392668,0.001049321,0.001004125,0.001208382,0.000251181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000300951,"about_ca_system_score_gemma":0.0005270738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007905222,"about_ca_topic_score_gemma":0.001524807,"domain_scores_codex":[0.9737691,0.01649946,0.002556399,0.004034142,0.002532362,0.0006085362],"domain_scores_gemma":[0.8428484,0.1444179,0.002666248,0.006950621,0.002419002,0.0006978394],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01035033,0.0003989473,0.7847829,0.0007704289,0.003718229,0.0007509027,0.0007436058,0.01089255,0.0660725,0.0007269259,0.002127411,0.1186652],"study_design_scores_gemma":[0.0005986515,0.004456495,0.8803471,0.000175134,0.002508779,0.001337156,0.0003701406,0.05724483,0.04348912,0.005340109,0.004001031,0.000131472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.94898,0.003490279,0.04339479,0.0008097806,0.0002875156,0.0002313759,0.001406157,0.0002844751,0.001115583],"genre_scores_gemma":[0.988268,0.0001959895,0.009587329,0.0002517529,0.0000403364,0.0002321111,0.001122367,0.00007461217,0.0002276412],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9470439,"threshold_uncertainty_score":0.2800621,"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."}}