{"id":"W2317715021","doi":"10.1021/pr501259e","title":"High-Performance Low-Cost Antibody Microarrays Using Enzyme-Mediated Silver Amplification","year":2015,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Biosensing Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University and Génome Québec Innovation Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canada Foundation for Innovation","keywords":"Multiplex; Protein microarray; Immunoassay; Detection limit; Microarray; Antibody microarray; DNA microarray; Molecular biology; Chemistry; Chromatography; Fluorescence; Protein Array Analysis; Biosensor; Antibody; Biology; Biochemistry; Gene expression; Bioinformatics; Gene; Immunology","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":[0.001364958,0.00009741439,0.0001384743,0.0001550632,0.0001240623,0.00003977967,0.0002803892,0.0001327865,0.000004004188],"category_scores_gemma":[0.000280388,0.00008173172,0.00004768069,0.0003237529,0.0001420449,0.00001693562,0.00009930257,0.0003406736,0.00001395817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008667095,"about_ca_system_score_gemma":0.0003084991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001288281,"about_ca_topic_score_gemma":9.665381e-7,"domain_scores_codex":[0.9987013,0.0001014896,0.0003245436,0.0001783527,0.0004081541,0.0002861782],"domain_scores_gemma":[0.998091,0.00001672858,0.0002115449,0.0003026901,0.001212314,0.0001657227],"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":[0.00011455,0.00007235689,0.0002799649,0.00001554799,0.00001273143,0.000003342294,0.00001818523,0.0001824284,0.9971057,0.00002461995,0.001213481,0.0009570745],"study_design_scores_gemma":[0.0004362455,0.000312793,0.0004907737,0.00006169438,0.000005020492,0.00008428218,0.00004870014,0.0004957914,0.9810986,0.0002095463,0.01664801,0.000108531],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9588926,0.0001756924,0.04006867,0.0003181568,0.00005536001,0.0004199792,0.0000104952,0.000008456396,0.0000505568],"genre_scores_gemma":[0.9600284,0.0002490678,0.03906461,0.00003128383,0.0004131024,0.00001222306,0.00004812496,0.00002073836,0.0001325131],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0160071,"threshold_uncertainty_score":0.3332923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08922858853332558,"score_gpt":0.4077218196018457,"score_spread":0.3184932310685201,"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."}}