{"id":"W2165372342","doi":"10.1517/17530059.1.3.325","title":"Lost in translation: five grand challenges for proteomic biomarker discovery","year":2007,"lang":"en","type":"article","venue":"Expert Opinion on Medical Diagnostics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; University of Toronto","funders":"","keywords":"Biomarker discovery; Profiling (computer programming); Disease; Medicine; Data science; Computational biology; Translational research; Bioinformatics; Computer science; Proteomics; Biology; Pathology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000327623,0.0001828623,0.0002045445,0.00007028539,0.00007204243,0.00001770649,0.0002261902,0.0003062814,0.0001557501],"category_scores_gemma":[0.0007781266,0.0001668709,0.000081657,0.00007794856,0.0001156108,0.00008692515,0.00002968957,0.0002558657,0.00001171863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007832858,"about_ca_system_score_gemma":0.00005757205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001301576,"about_ca_topic_score_gemma":0.00002777189,"domain_scores_codex":[0.9985773,0.00001173053,0.0004033033,0.0003610813,0.0003321256,0.0003144353],"domain_scores_gemma":[0.9980268,0.001412487,0.00008070482,0.0002736069,0.00003766434,0.0001686884],"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":[0.001336093,0.002554032,0.001232094,0.0004966109,0.00007275181,0.00006143601,0.002647283,0.00003662918,0.01383904,0.06995997,0.01530382,0.8924602],"study_design_scores_gemma":[0.005436556,0.0003081146,0.001940318,0.001843461,0.000006291802,0.00001972232,0.0005851524,0.00285295,0.1676841,0.01703323,0.8012482,0.001041876],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03384151,0.02067006,0.8920302,0.03317188,0.0008043293,0.003203797,0.0002994824,0.0003913504,0.0155874],"genre_scores_gemma":[0.6281852,0.2563071,0.101647,0.003756459,0.003559679,0.004643815,0.0009809835,0.0002592229,0.0006605881],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8914183,"threshold_uncertainty_score":0.6804798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0468280847069817,"score_gpt":0.3566925073629691,"score_spread":0.3098644226559875,"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."}}