{"id":"W1998719716","doi":"10.1021/pr101080e","title":"Data Variance and Statistical Significance in 2D-Gel Electrophoresis and DIGE Experiments: Comparison of the Effects of Normalization Methods","year":2010,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Normalization (sociology); Difference gel electrophoresis; Standardization; Analysis of variance; Computer science; Artificial intelligence; Biological system; Pattern recognition (psychology); Statistics; Mathematics; Proteomics; Chemistry; Biology","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":[0.001411113,0.00005106198,0.000142776,0.0000757549,0.0000389311,0.00001364482,0.0002838904,0.00007203671,0.000004649311],"category_scores_gemma":[0.001173869,0.00003520182,0.00001287086,0.0001676092,0.0001840492,0.0000105093,0.0001722996,0.000263376,4.642185e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008709662,"about_ca_system_score_gemma":0.0001496322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000174035,"about_ca_topic_score_gemma":0.0000120594,"domain_scores_codex":[0.9987256,0.0004636981,0.0002877471,0.0001416432,0.0002666121,0.0001146923],"domain_scores_gemma":[0.9991351,0.00011258,0.0002047567,0.0002911905,0.000207703,0.0000486169],"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":[0.0001616725,0.00008679424,0.0075663,0.0001139913,0.00001185733,4.017509e-7,0.00009806926,0.000001710678,0.9871811,0.0001486128,0.0002837702,0.004345691],"study_design_scores_gemma":[0.0004029619,0.0003292413,0.04866258,0.00005566342,0.000005283194,0.000004041437,0.00008443557,0.0003509265,0.9486445,0.0001858644,0.001240153,0.0000343231],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9688765,0.00199885,0.02855515,0.000134784,0.00006959232,0.0003222272,0.000009917485,4.213871e-7,0.0000326033],"genre_scores_gemma":[0.9852277,0.0005063189,0.01417127,0.00000472064,0.00004114911,0.00001312181,0.000003881616,0.000005551801,0.00002626877],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04109628,"threshold_uncertainty_score":0.1435489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06562119110901933,"score_gpt":0.4739692106198079,"score_spread":0.4083480195107886,"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."}}