{"id":"W2005497218","doi":"10.1016/j.jprot.2010.04.003","title":"The cancer cell secretome: A good source for discovering biomarkers?","year":2010,"lang":"en","type":"review","venue":"Journal of Proteomics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":202,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Health Network; University of Toronto; Mount Sinai Hospital","funders":"","keywords":"Biomarker discovery; Proteomics; Biomarker; Cancer; Cancer biomarkers; Prostate cancer; Pancreatic cancer; Medicine; Colorectal cancer; Breast cancer; Computational biology; Bioinformatics; 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":[],"consensus_categories":[],"category_scores_codex":[0.0005004667,0.0003715845,0.0009182521,0.0000445051,0.000316104,0.000156846,0.00112737,0.0004785929,0.00003771507],"category_scores_gemma":[0.00009146275,0.0002360249,0.0008914362,0.0001118227,0.0000874327,0.0001283132,0.0001545592,0.001215856,0.000001650007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003563726,"about_ca_system_score_gemma":0.0005742806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001623344,"about_ca_topic_score_gemma":0.00001315718,"domain_scores_codex":[0.9981293,0.0000175518,0.001058636,0.0002563278,0.0002007743,0.0003374285],"domain_scores_gemma":[0.9961594,0.000295533,0.002665878,0.0005678514,0.0001867429,0.0001245381],"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":[0.00004700186,0.00005882134,0.000001440249,0.008208216,0.0002507112,0.000002075456,0.0000379937,0.00001038422,0.00864567,0.0004595099,0.0005337396,0.9817444],"study_design_scores_gemma":[0.0002069333,0.00002590514,1.34816e-8,0.00308867,0.0004350917,0.00008062232,0.00004432439,0.00004440597,0.009810586,0.0008738874,0.9851094,0.0002801833],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00003475164,0.8827258,0.1152393,0.00007292047,0.0001512752,0.00102826,0.0002388642,0.00002718919,0.0004816244],"genre_scores_gemma":[0.000002530586,0.8737998,0.1231262,0.000007576159,0.0008922804,0.001038842,0.00001277107,0.0001114482,0.00100849],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9845756,"threshold_uncertainty_score":0.9624816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02322681819782219,"score_gpt":0.3413653621722245,"score_spread":0.3181385439744023,"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."}}