{"id":"W2156344403","doi":"10.1074/mcp.m112.022442","title":"Multiplexed Quantitation of Endogenous Proteins in Dried Blood Spots by Multiple Reaction Monitoring - Mass Spectrometry","year":2012,"lang":"en","type":"article","venue":"Molecular & Cellular Proteomics","topic":"Biosimilars and Bioanalytical Methods","field":"Immunology and Microbiology","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"Genome British Columbia; University of Victoria","funders":"","keywords":"Dried blood; Analyte; Chromatography; Chemistry; Selected reaction monitoring; Mass spectrometry; Dried blood spot; Biomarker discovery; Quantitative proteomics; Tandem mass spectrometry; Label-free quantification; Population; Small molecule; Whole blood; Multiplex; Sample preparation; Immunoassay; Blood sampling; Proteomics; Bioinformatics; Biochemistry; Biology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001842289,0.001226838,0.0009283911,0.001773855,0.0003459107,0.001004394,0.0007829078,0.0008565987,0.0006318947],"category_scores_gemma":[0.001524749,0.0005126987,0.0005349712,0.001079862,0.0006178375,0.0005853926,0.0006764843,0.0009385559,0.0007765357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004069915,"about_ca_system_score_gemma":0.0003611813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003990521,"about_ca_topic_score_gemma":0.0007126769,"domain_scores_codex":[0.9972453,0.000431886,0.0001479634,0.001020383,0.001012375,0.0001420443],"domain_scores_gemma":[0.9993297,0.0001627027,0.0001958728,0.00006684267,0.0001909568,0.00005401564],"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":[0.0001805773,0.00008158078,0.0008240922,0.0001382093,0.00006005513,0.000104745,0.00007870967,0.00027824,0.9868058,0.0001628619,0.0002510735,0.01103406],"study_design_scores_gemma":[0.00002755727,0.0002983358,0.00453453,0.00001877892,0.0000636215,0.0005244152,0.00003635156,0.009492813,0.9822406,0.0002482852,0.002468586,0.00004620831],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6182464,0.009236558,0.3626941,0.0003270891,0.000451567,0.0006632568,0.002319025,0.003163203,0.002898816],"genre_scores_gemma":[0.5366427,0.007253918,0.4468126,0.0006317194,0.0002216561,0.001274667,0.001726163,0.0002061245,0.005230445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001842289,"threshold_uncertainty_score":0.009743094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02608756240591608,"score_gpt":0.2511941104659813,"score_spread":0.2251065480600652,"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."}}