{"id":"W2013679887","doi":"10.1002/prca.200600802","title":"An experimental strategy for quantitative analysis of the humoral immune response to prostate cancer antigens using natural protein microarrays","year":2007,"lang":"en","type":"article","venue":"PROTEOMICS - CLINICAL APPLICATIONS","topic":"Advanced Biosensing Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Cancer Institute; National Institutes of Health; Van Andel Research Institute; Michigan Economic Development Corporation","keywords":"Prostate cancer; Antigen; Immune system; Proteomics; Biology; DNA microarray; Cancer; Protein microarray; Tissue microarray; Cancer biomarkers; Cancer research; Immunohistochemistry; Computational biology; Immunology; Pathology; Medicine; Gene expression; Biochemistry; Gene; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007621794,0.0008099801,0.0004575538,0.0004199996,0.0003299633,0.0004826075,0.0004809716,0.0005995273,0.001091686],"category_scores_gemma":[0.0007208881,0.0003926885,0.0003657891,0.0003890065,0.0004371447,0.0004056217,0.0003764153,0.001261368,0.0006235226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003479779,"about_ca_system_score_gemma":0.0003601127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002931254,"about_ca_topic_score_gemma":0.0006230379,"domain_scores_codex":[0.9993755,0.0001968372,0.00003669813,0.0001408813,0.0001905629,0.00005948536],"domain_scores_gemma":[0.9996859,0.0001199313,0.00005389887,0.00005894384,0.0000552299,0.00002625679],"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":[0.00002080847,0.00001911883,0.00004374045,0.00003596728,0.000003613557,0.000007178154,0.000008557722,0.0000438393,0.9982039,0.0001536261,0.00003588735,0.001423816],"study_design_scores_gemma":[0.00001640942,0.0003053968,0.001152627,0.000008143822,0.0000134668,0.0001542382,0.00001571692,0.001994497,0.9928761,0.0003190486,0.00313207,0.00001227776],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2529159,0.002453216,0.7362933,0.00067631,0.0002725892,0.001284056,0.001153363,0.001438741,0.003512655],"genre_scores_gemma":[0.3866117,0.002474779,0.6006805,0.0007412565,0.0000969505,0.003827738,0.001444003,0.0001048513,0.004018092],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001091686,"threshold_uncertainty_score":0.004030824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05722771874865967,"score_gpt":0.4573000284480701,"score_spread":0.4000723096994104,"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."}}