{"id":"W3204471759","doi":"10.1016/j.mcpro.2021.100155","title":"Uncovering the Depths of the Human Proteome: Antibody-based Technologies for Ultrasensitive Multiplexed Protein Detection and Quantification","year":2021,"lang":"en","type":"review","venue":"Molecular & Cellular Proteomics","topic":"Advanced Biosensing Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Lunenfeld-Tanenbaum Research Institute; University of Toronto; Mount Sinai Hospital","funders":"","keywords":"Multiplex; Proteomics; Proteome; Computational biology; Quantitative proteomics; Human proteome project; Computer science; Biomarker discovery; Complement (music); Biomarker; Nanotechnology; Data science; Chemistry; Bioinformatics; Biology; Materials science; 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":[0.0003102812,0.0003813878,0.0005422974,0.00006070817,0.0003483141,0.00004990325,0.0003798418,0.0005109846,1.608468e-7],"category_scores_gemma":[0.0002808006,0.0002605726,0.0004482833,0.0002957215,0.0003310379,0.000003800712,0.0002158233,0.0003097201,2.334097e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004216176,"about_ca_system_score_gemma":0.0001611443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001093918,"about_ca_topic_score_gemma":0.000007913812,"domain_scores_codex":[0.9983065,0.0001701978,0.0004897718,0.0006438914,0.0001483846,0.0002412592],"domain_scores_gemma":[0.9979674,0.00002469705,0.0006578051,0.001105566,0.0002204248,0.00002410442],"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":[0.000006591867,0.00003556142,5.393863e-7,0.001311353,0.00006128648,0.000001045915,0.000003220203,0.00002892832,0.9522862,0.0002455141,0.000002584711,0.04601711],"study_design_scores_gemma":[0.0001612669,0.00008279856,7.796032e-7,0.001305185,0.0001923219,0.00001267485,0.00002400468,0.00007794733,0.9231836,0.0001791472,0.07449853,0.0002817119],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.03520584,0.5182262,0.4330383,0.0001292384,0.00004986731,0.01308789,0.000158187,0.00009560586,0.000008850013],"genre_scores_gemma":[0.4077782,0.4425206,0.1368027,0.0000824513,0.0001967598,0.009461321,0.002520221,0.0004460981,0.0001917032],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.3725724,"threshold_uncertainty_score":0.9999846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0193374434641805,"score_gpt":0.3100486383238218,"score_spread":0.2907111948596413,"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."}}