{"id":"W1993632446","doi":"10.1021/pr800501j","title":"iTRAQ-Multidimensional Liquid Chromatography and Tandem Mass Spectrometry-Based Identification of Potential Biomarkers of Oral Epithelial Dysplasia and Novel Networks between Inflammation and Premalignancy","year":2008,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Oral Health Pathology and Treatment","field":"Dentistry","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Inflammation; Dysplasia; Proteome; Proteomics; Biomarker; Epithelial dysplasia; Biomarker discovery; Biology; Immunohistochemistry; Isobaric labeling; OPLS; Tandem mass spectrometry; Cancer; Bioinformatics; Cancer research; Computational biology; Pathology; Quantitative proteomics; Medicine; Immunology; Chemistry; Gene; Mass spectrometry; Biochemistry; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007181006,0.0006445828,0.0004321809,0.001490447,0.000222553,0.000512149,0.0002629985,0.0004421445,0.0004507448],"category_scores_gemma":[0.0006512351,0.0002000764,0.0003775337,0.0006881619,0.000321058,0.0003431397,0.0002815878,0.0003220024,0.0002395534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002553268,"about_ca_system_score_gemma":0.0003612274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005877575,"about_ca_topic_score_gemma":0.000886545,"domain_scores_codex":[0.9995711,0.0001006376,0.00003718565,0.0001008616,0.0001425671,0.00004770494],"domain_scores_gemma":[0.9997254,0.00005659323,0.000108615,0.00001748731,0.00005883165,0.0000330764],"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":[0.0002771925,0.00006251859,0.008129146,0.0001011824,0.00005035174,0.0001190071,0.00003303168,0.0002256286,0.9833868,0.00009855836,0.00006968428,0.007446809],"study_design_scores_gemma":[0.00006366297,0.0008907877,0.165418,0.00003799895,0.0001575302,0.002837661,0.0001138817,0.01223946,0.8150886,0.0005604888,0.002542621,0.00004933771],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9737219,0.004297473,0.01990426,0.0001617121,0.00002210378,0.0001267446,0.0009556999,0.000178628,0.0006315384],"genre_scores_gemma":[0.9483876,0.001608839,0.04673417,0.0002607129,0.00002512699,0.0001763719,0.001778868,0.00002333821,0.00100498],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001490447,"threshold_uncertainty_score":0.00379771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0475419132779241,"score_gpt":0.3478985542699109,"score_spread":0.3003566409919868,"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."}}