{"id":"W1970761334","doi":"10.1631/jzus.2006.b0411","title":"Proteomic technology for biomarker profiling in cancer: an update","year":2006,"lang":"en","type":"review","venue":"Journal of Zhejiang University SCIENCE B","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Jewish General Hospital","funders":"National Cancer Institute; Canadian Institutes of Health Research; Canadian Breast Cancer Research Alliance; Cancer Research Society","keywords":"Proteome; Computational biology; Proteomics; Biomarker discovery; Cancer; Biomarker; Biology; Cancer biomarkers; Bioinformatics; Human proteome project; Gene; Genetics","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":[0.002539771,0.002045654,0.002662642,0.005048304,0.0004603103,0.002228089,0.002066029,0.002757988,0.003434022],"category_scores_gemma":[0.002123102,0.0007110294,0.001018691,0.004588028,0.001114824,0.003678051,0.0009702459,0.003103988,0.005216544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001011383,"about_ca_system_score_gemma":0.001131027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001013145,"about_ca_topic_score_gemma":0.001345427,"domain_scores_codex":[0.9987252,0.0002503292,0.0001882528,0.00017262,0.0005992305,0.00006437599],"domain_scores_gemma":[0.9978943,0.0009893427,0.0001619466,0.0001026635,0.0007035825,0.0001480767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009528102,0.0001685195,0.0005634889,0.009846587,0.0001449344,0.000865148,0.0001060533,0.00041535,0.006299905,0.003967619,0.04064195,0.9368851],"study_design_scores_gemma":[0.00003047574,0.0001936076,0.001210361,0.001635402,0.0001297267,0.005372999,0.00007507723,0.0004200744,0.002424663,0.001969269,0.9864861,0.00005245361],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002544747,0.9941339,0.001789483,0.0006697345,0.001230106,0.00001994612,0.00002700299,0.00003146383,0.001843876],"genre_scores_gemma":[0.0009985223,0.9924272,0.002884654,0.0006780041,0.001146317,0.00003294343,0.00006586885,0.000008876696,0.001757671],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005048304,"threshold_uncertainty_score":0.01343173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04148052899865682,"score_gpt":0.3581193156945942,"score_spread":0.3166387866959373,"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."}}