{"id":"W1967187739","doi":"10.1586/14789450.4.2.175","title":"Coupling immunoaffinity techniques with MS for quantitative analysis of low-abundance protein biomarkers","year":2007,"lang":"en","type":"review","venue":"Expert Review of Proteomics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":176,"is_retracted":false,"has_abstract":true,"ca_institutions":"Greenfield Research (Canada)","funders":"University of Pennsylvania; Eli Lilly and Company","keywords":"Quantitative proteomics; Chemistry; Proteomics; Chromatography; Selected reaction monitoring; Peptide; Mass spectrometry; Tandem mass spectrometry; Quantitative analysis (chemistry); Liquid chromatography–mass spectrometry; Analyte; Biochemistry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001084354,0.000741079,0.004249647,0.0003908354,0.0001143895,0.00001773176,0.0009507817,0.0004703715,0.00007551828],"category_scores_gemma":[0.0002997632,0.0005795883,0.00154722,0.001722437,0.0003467838,0.0001071336,0.0001470739,0.0004705757,0.000001130201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002687947,"about_ca_system_score_gemma":0.0004116291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006001441,"about_ca_topic_score_gemma":0.000008928949,"domain_scores_codex":[0.9961745,0.00003758128,0.002255771,0.0007822636,0.0003499394,0.0003999379],"domain_scores_gemma":[0.9937477,0.0003331591,0.00373349,0.001426426,0.0006713244,0.00008793444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002060981,0.0004334306,0.000001907674,0.6276137,0.005116307,0.00000238294,0.00006791718,0.000004902319,0.01862383,0.004147128,0.0001128553,0.3436696],"study_design_scores_gemma":[0.0001725044,0.0002112743,6.747732e-8,0.3554502,0.003367339,0.000005582505,0.00002289588,0.00016572,0.1235856,0.0001429367,0.5159009,0.0009750713],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00001128334,0.7154151,0.2778381,0.00001309605,0.000007437204,0.005876072,0.0004836008,0.0001024307,0.0002529246],"genre_scores_gemma":[7.588344e-7,0.5375888,0.455271,0.00001263764,0.00002782371,0.006598517,0.0003980249,0.00007468515,0.00002778521],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.515788,"threshold_uncertainty_score":0.9996656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04784550010769109,"score_gpt":0.4129522565740281,"score_spread":0.3651067564663369,"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."}}