{"id":"W2045704125","doi":"10.1016/j.jchromb.2006.09.004","title":"Global and targeted quantitative proteomics for biomarker discovery","year":2006,"lang":"en","type":"review","venue":"Journal of Chromatography B","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":116,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Cancer Institute; European Bioinformatics Institute; Canadian Institute for Theoretical Astrophysics","keywords":"Biomarker discovery; Proteomics; Proteome; Biomarker; Computational biology; Chemistry; Quantitative proteomics; Data science; Computer science; Biology; Biochemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001671608,0.002420845,0.002701677,0.002149119,0.000337471,0.001168889,0.002279042,0.001762308,0.002537762],"category_scores_gemma":[0.0007605036,0.0007960743,0.000576974,0.00291159,0.001129595,0.002632411,0.00134839,0.002193307,0.003878573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007791644,"about_ca_system_score_gemma":0.0009905659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003994108,"about_ca_topic_score_gemma":0.0007647233,"domain_scores_codex":[0.9994106,0.00007461746,0.0000421278,0.0001098696,0.0003185761,0.00004416051],"domain_scores_gemma":[0.9995202,0.000157808,0.00006618165,0.00004480219,0.0001675357,0.00004355583],"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":[0.0001426366,0.0001024802,0.00008287174,0.005949158,0.0001124062,0.0001757183,0.00002524848,0.000847183,0.03084814,0.009176632,0.03920081,0.9133368],"study_design_scores_gemma":[0.00003083452,0.0001395865,0.0003815819,0.0004410611,0.0001120378,0.001241184,0.00002185375,0.0009328439,0.02143393,0.007084527,0.9681352,0.00004536623],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000625938,0.960677,0.03048658,0.0009446791,0.003012845,0.00004502215,0.00008509598,0.000224911,0.00389795],"genre_scores_gemma":[0.003384964,0.9709927,0.01572481,0.001006023,0.001566297,0.00006717413,0.0001571556,0.00003067685,0.007070305],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002701677,"threshold_uncertainty_score":0.008840442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03264808115300277,"score_gpt":0.3557115993219402,"score_spread":0.3230635181689375,"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."}}