{"id":"W2128789834","doi":"10.1373/clinchem.2009.127019","title":"The Bottleneck in the Cancer Biomarker Pipeline and Protein Quantification through Mass Spectrometry–Based Approaches: Current Strategies for Candidate Verification","year":2009,"lang":"en","type":"review","venue":"Clinical Chemistry","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":169,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Mount Sinai Hospital; University of Toronto","funders":"","keywords":"Biomarker discovery; Analyte; Selected reaction monitoring; Biomarker; Multiplex; Quantitative proteomics; Computer science; Chromatography; Mass spectrometry; Computational biology; Chemistry; Proteomics; Tandem mass spectrometry; Bioinformatics; Biology","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":[0.01658256,0.001244466,0.002306943,0.002120438,0.0007852445,0.003877455,0.002397306,0.002028808,0.003015272],"category_scores_gemma":[0.008685428,0.0006929616,0.001254038,0.002002835,0.002987787,0.005702597,0.002231993,0.003864267,0.002741903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002491487,"about_ca_system_score_gemma":0.004528829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001117963,"about_ca_topic_score_gemma":0.001356673,"domain_scores_codex":[0.9949812,0.001374539,0.000377906,0.0009870558,0.002059459,0.0002198707],"domain_scores_gemma":[0.9898744,0.004589224,0.001177317,0.000710206,0.003248954,0.0003999886],"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":[0.0008880631,0.0002186184,0.00387606,0.01052736,0.0003046817,0.0004664452,0.0004234931,0.002468553,0.1658567,0.05047548,0.02287475,0.7416198],"study_design_scores_gemma":[0.00009513641,0.002099206,0.004232927,0.003215669,0.0005254941,0.002335535,0.0006714381,0.01391758,0.2225257,0.06158603,0.6885114,0.0002838234],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.01853852,0.6087095,0.3317232,0.02880097,0.00159744,0.0005706515,0.001037799,0.001467134,0.007554812],"genre_scores_gemma":[0.09786769,0.5079773,0.3655028,0.01378735,0.001878022,0.001178602,0.002262306,0.0004027952,0.009143163],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01658256,"threshold_uncertainty_score":0.08769804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2137148214165347,"score_gpt":0.4544571271764846,"score_spread":0.2407423057599499,"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."}}