{"id":"W2057666594","doi":"10.1586/14789450.5.4.551","title":"Advances in ovarian cancer proteomics: the quest for biomarkers and improved therapeutic interventions","year":2008,"lang":"en","type":"review","venue":"Expert Review of Proteomics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research; University of Toronto","funders":"","keywords":"Biomarker discovery; Proteomics; Ovarian cancer; Biomarker; Disease; Cancer; Bioinformatics; Medicine; Computational biology; Biology; Internal medicine","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":[0.0004150919,0.0006022733,0.001905326,0.0001035895,0.0001775873,0.0000273477,0.0007884908,0.0003320247,0.00005848848],"category_scores_gemma":[0.0001566427,0.0004117447,0.0009587111,0.0003245744,0.0002915764,0.0001608096,0.0001818886,0.0005231407,0.000001236375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002500092,"about_ca_system_score_gemma":0.0003350272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006934629,"about_ca_topic_score_gemma":0.00004213721,"domain_scores_codex":[0.997088,0.00009044327,0.001618941,0.0006951895,0.000135902,0.0003715775],"domain_scores_gemma":[0.9972093,0.0002105685,0.001459775,0.0009101662,0.0001355062,0.00007470018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001834226,0.00009021114,0.000001323002,0.1717024,0.0001163813,5.418954e-7,0.00004430681,1.981396e-7,0.0007947041,0.001034862,0.0001613877,0.8260353],"study_design_scores_gemma":[0.0002380612,0.0000379854,9.527867e-8,0.1642541,0.0001823926,0.00003192766,0.00001427724,0.00003217064,0.001219099,0.0008571607,0.8327036,0.000429151],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[2.944534e-7,0.9442392,0.04557484,0.0003638788,0.00005929996,0.009180231,0.0002978741,0.00005052457,0.0002338343],"genre_scores_gemma":[4.508589e-7,0.822311,0.1359093,0.0001218205,0.0001552946,0.04121239,0.0001170736,0.0000968832,0.00007581026],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8325422,"threshold_uncertainty_score":0.9998335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0436088064293049,"score_gpt":0.4149584231090774,"score_spread":0.3713496166797725,"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."}}