{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001359573,0.001176323,0.0006989377,0.0005891046,0.0002174538,0.0009926278,0.00116043,0.001240932,0.001073566],"category_scores_gemma":[0.001159605,0.0006952957,0.0006246665,0.0004563666,0.0004906356,0.0009051886,0.0007101883,0.0008937964,0.001548818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005010398,"about_ca_system_score_gemma":0.0002646256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002430226,"about_ca_topic_score_gemma":0.0005177986,"domain_scores_codex":[0.9979248,0.0004819331,0.0001081591,0.0005009513,0.0008727277,0.0001114979],"domain_scores_gemma":[0.9993085,0.000317354,0.0001169273,0.00006318657,0.0001602941,0.00003371702],"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":[0.00003837916,0.00001794337,0.0002026488,0.0001119968,0.00001630177,0.00002886736,0.00001311574,0.0002317637,0.9899361,0.0001775681,0.0001594907,0.009065949],"study_design_scores_gemma":[0.00001270743,0.0001774236,0.001092919,0.0000125261,0.0000259418,0.0002846459,0.00001261734,0.005960514,0.9876846,0.0002579692,0.004454306,0.0000237727],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2191722,0.008972707,0.7622414,0.000754195,0.0003305951,0.0003297538,0.0004337484,0.003777552,0.003987933],"genre_scores_gemma":[0.3262795,0.004263664,0.6617544,0.0005484773,0.0001178825,0.0005427094,0.0007037892,0.000138737,0.005650981],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001359573,"threshold_uncertainty_score":0.007190168,"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."}}