{"id":"W2165384556","doi":"10.1074/mcp.r400004-mcp200","title":"HUPO Initiatives Relevant to Clinical Proteomics","year":2004,"lang":"en","type":"review","venue":"Molecular & Cellular Proteomics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Proteomics; Computational biology; Library science; Data science; Computer science; Biology; Genetics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04067784,0.001768886,0.0009583813,0.003394684,0.003795983,0.01634112,0.004343347,0.009003838,0.08543827],"category_scores_gemma":[0.02998377,0.0008508397,0.001305185,0.003345758,0.003374142,0.007625701,0.01535483,0.009395648,0.05871752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006389732,"about_ca_system_score_gemma":0.0527005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004391659,"about_ca_topic_score_gemma":0.003993073,"domain_scores_codex":[0.9830464,0.004003537,0.0008660535,0.001913365,0.006962935,0.003207686],"domain_scores_gemma":[0.9181505,0.00526891,0.003658857,0.006206268,0.02077628,0.04593908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002398732,0.0003432714,0.001300486,0.0005234593,0.00002660585,0.0003667256,0.0002878398,0.0001237312,0.00193946,0.0532996,0.8082027,0.1333463],"study_design_scores_gemma":[0.00003619184,0.00007089228,0.001122385,0.0003091408,0.000006680033,0.0002044842,0.0001545705,0.0001379789,0.0005576439,0.005621774,0.9917638,0.00001454374],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"review","genre_scores_codex":[0.003907877,0.02489215,0.03384587,0.5023658,0.06854662,0.002199289,0.004870716,0.006648521,0.3527232],"genre_scores_gemma":[0.04182475,0.04837985,0.1253402,0.2198937,0.04126463,0.002622169,0.01676119,0.003680531,0.500233],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.08543827,"threshold_uncertainty_score":0.2858195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04134999662326405,"score_gpt":0.370148911902081,"score_spread":0.328798915278817,"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."}}