{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05256652,0.002016905,0.002958015,0.002345671,0.002026321,0.0125223,0.003445041,0.009635076,0.008846765],"category_scores_gemma":[0.08205534,0.0009243847,0.001457062,0.001910045,0.008785331,0.01703034,0.006762266,0.01506605,0.007964734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003792205,"about_ca_system_score_gemma":0.008675445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001765635,"about_ca_topic_score_gemma":0.001676927,"domain_scores_codex":[0.9863469,0.00586359,0.001606861,0.001213733,0.004167582,0.0008013373],"domain_scores_gemma":[0.9315776,0.04103855,0.002694941,0.005177859,0.01538762,0.004123542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005731781,0.0001742109,0.001736457,0.005610628,0.0002351148,0.001277908,0.001295098,0.0008254457,0.003450883,0.1218079,0.3111035,0.5519097],"study_design_scores_gemma":[0.0001251749,0.0004740641,0.001577742,0.003726091,0.0001391694,0.002197077,0.003071269,0.001905971,0.002280378,0.2667359,0.7175683,0.000198796],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.002082997,0.2389613,0.02104247,0.7081078,0.02250365,0.00007010684,0.0002488912,0.0003999029,0.006582798],"genre_scores_gemma":[0.07133629,0.5246341,0.09477232,0.2363975,0.0570263,0.0004207215,0.0008597071,0.0006003159,0.01395277],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05256652,"threshold_uncertainty_score":0.2780016,"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."}}