{"id":"W2148104731","doi":"10.1074/mcp.m700500-mcp200","title":"Discovery and Verification of Head-and-neck Cancer Biomarkers by Differential Protein Expression Analysis Using iTRAQ Labeling, Multidimensional Liquid Chromatography, and Tandem Mass Spectrometry","year":2008,"lang":"en","type":"article","venue":"Molecular & Cellular Proteomics","topic":"14-3-3 protein interactions","field":"Biochemistry, Genetics and Molecular Biology","cited_by":219,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Biology; Biomarker discovery; Biomarker; Tandem mass spectrometry; Molecular biology; Cancer; Proteomics; Chemistry; Cancer research; Biochemistry; Mass spectrometry; Gene; Genetics; Chromatography","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001113453,0.0002695386,0.0003155787,0.0002083589,0.0001869853,0.00003484684,0.00009787793,0.0002257979,0.00000982539],"category_scores_gemma":[0.00003732806,0.0002718393,0.0001484093,0.0002818835,0.0002564852,0.0000245976,0.0001390454,0.0001477151,1.897757e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002882632,"about_ca_system_score_gemma":0.00006226351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001966118,"about_ca_topic_score_gemma":0.000008597711,"domain_scores_codex":[0.9984422,0.000125596,0.0003491233,0.0006092396,0.0002246343,0.0002491874],"domain_scores_gemma":[0.9992141,0.000007454492,0.0002429247,0.0003399568,0.00008788615,0.0001076854],"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":[0.000229465,0.00009349841,0.004899934,0.0000536413,0.0005299948,0.000007516423,0.00004037131,0.00004857067,0.9940478,0.00001473629,0.00001044942,0.00002398222],"study_design_scores_gemma":[0.0007380754,0.0002278685,0.001194341,0.0000797121,0.0002205979,0.00001713781,0.00002657624,0.001488496,0.9956604,0.00001645883,0.00004507694,0.000285321],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8974832,0.003882658,0.09780234,0.00002976749,0.00003617293,0.0006736985,0.00007518724,0.00001137588,0.000005660809],"genre_scores_gemma":[0.9655664,0.000560193,0.03346749,0.00001847867,0.000037604,0.00009406161,0.0001838477,0.00003864371,0.0000333159],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06808323,"threshold_uncertainty_score":0.9999734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008495805162051015,"score_gpt":0.242383141616675,"score_spread":0.233887336454624,"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."}}