{"id":"W1992396342","doi":"10.1002/pmic.201200316","title":"Multiplexed <scp>MRM</scp>‐based quantitation of candidate cancer biomarker proteins in undepleted and non‐enriched human plasma","year":2013,"lang":"en","type":"article","venue":"PROTEOMICS","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"Genome British Columbia; University of Victoria","funders":"University of British Columbia; Genome British Columbia; Genome Canada","keywords":"Multiplex; Biomarker; Biomarker discovery; Quantitative proteomics; Selected reaction monitoring; Cancer biomarkers; Chemistry; Proteomics; Chromatography; Reproducibility; Immunoassay; Cancer; Computational biology; Biology; Bioinformatics; Mass spectrometry; Tandem mass spectrometry; Biochemistry; Immunology; Antibody","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":[],"consensus_categories":[],"category_scores_codex":[0.0001256845,0.0002260569,0.0002840903,0.0001354665,0.000106078,0.00004108725,0.0002007109,0.0001998091,0.00005590453],"category_scores_gemma":[0.0001058205,0.0002308786,0.00005037536,0.0002660418,0.0001226449,0.0001623903,0.00007287873,0.0002371173,0.000005712441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001219628,"about_ca_system_score_gemma":0.00007990588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00299848,"about_ca_topic_score_gemma":0.0005907424,"domain_scores_codex":[0.9986776,0.00001909094,0.0004729228,0.0003836352,0.0001440319,0.0003027882],"domain_scores_gemma":[0.9989698,0.00008631613,0.0003504479,0.0003654529,0.000139554,0.00008843475],"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":[0.00001546626,0.00009446809,0.01536231,0.0002105055,0.00001619144,6.989961e-7,0.000152291,0.0001642531,0.9828195,0.0004135345,0.00004624336,0.0007045183],"study_design_scores_gemma":[0.001587259,0.00003295759,0.007656808,0.0001250957,0.00001458085,0.000001415474,0.0001259606,0.04225593,0.9447337,0.003119365,0.0001863026,0.0001605869],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9822428,0.00004283908,0.01506624,0.0001308458,0.00001000668,0.001922384,0.00008124096,0.0001009429,0.0004027429],"genre_scores_gemma":[0.862359,0.00003710809,0.1335223,0.00002954244,0.00001782709,0.003758036,0.00007854788,0.00004408966,0.0001535165],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1198838,"threshold_uncertainty_score":0.9414957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02091170805676199,"score_gpt":0.2918418121600279,"score_spread":0.2709301041032659,"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."}}