{"id":"W2032376737","doi":"10.1038/clpt.2008.18","title":"Mass Spectrometry–Based Proteomics: A Useful Tool for Biomarker Discovery?","year":2008,"lang":"en","type":"article","venue":"Clinical Pharmacology & Therapeutics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research; University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Biomarker discovery; False positive paradox; Biomarker; Proteomics; Computational biology; Mass spectrometry; False positives and false negatives; Drug discovery; Computer science; Bioinformatics; Chemistry; Biology; Chromatography; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003014157,0.0002852228,0.0004290047,0.00005897173,0.0002721926,0.00002350666,0.0005068867,0.0003807437,0.0003554423],"category_scores_gemma":[0.00002852643,0.0002706679,0.0003881417,0.0001832011,0.0005397869,0.0001157334,0.00006762728,0.0005451076,0.00002961737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001044341,"about_ca_system_score_gemma":0.0001835664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.570358e-7,"about_ca_topic_score_gemma":2.354036e-7,"domain_scores_codex":[0.9979805,0.00005403642,0.0007519872,0.000598354,0.0001285998,0.0004864775],"domain_scores_gemma":[0.9981443,0.0008223723,0.0003069248,0.0004919871,0.000120813,0.0001136472],"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":[0.0007471534,0.0006891193,0.009420817,0.00004552096,0.0003408352,0.0000104814,0.00001366136,0.00002867599,0.9831325,0.0001885419,0.004775673,0.000607057],"study_design_scores_gemma":[0.004536859,0.0002406611,0.0006643988,0.00001732317,0.0004734679,0.000018972,0.00001062626,0.006586391,0.8737124,0.006873494,0.1062137,0.0006517624],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4050045,0.0001282521,0.5889933,0.003703614,0.0002386467,0.00101203,0.0001764187,0.0002998553,0.0004433904],"genre_scores_gemma":[0.4323921,0.0008956997,0.537973,0.02394409,0.0009439366,0.001904774,0.00009755404,0.000128064,0.001720722],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1094201,"threshold_uncertainty_score":0.9999745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1052692145469171,"score_gpt":0.4154754309778496,"score_spread":0.3102062164309324,"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."}}