{"id":"W2028369276","doi":"10.1002/prca.200800030","title":"High‐resolution biomarker discovery: Moving from large‐scale proteome profiling to quantitative validation of lead candidates","year":2008,"lang":"en","type":"article","venue":"PROTEOMICS - CLINICAL APPLICATIONS","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Profiling (computer programming); Biomarker discovery; Context (archaeology); Proteomics; Computer science; Proteome; Computational biology; Biomarker; Precision medicine; Data science; Personalized medicine; Bioinformatics; Medicine; 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":[0.007544999,0.001279871,0.002155283,0.001719871,0.0004526486,0.003093972,0.001205041,0.001155386,0.001049562],"category_scores_gemma":[0.004860314,0.0004883771,0.0005975426,0.001433525,0.001309781,0.002035567,0.001196533,0.001670672,0.0009474619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008348014,"about_ca_system_score_gemma":0.001341752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003154053,"about_ca_topic_score_gemma":0.0006231183,"domain_scores_codex":[0.9972599,0.001371813,0.000134494,0.0004024278,0.000720337,0.000111128],"domain_scores_gemma":[0.9974065,0.001187988,0.000421948,0.0003511255,0.0004765766,0.0001558646],"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":[0.0005957829,0.0004219121,0.004011555,0.001330033,0.0002696287,0.0003889458,0.0001760035,0.00495844,0.7055851,0.01076724,0.002634788,0.2688605],"study_design_scores_gemma":[0.000319369,0.002332371,0.007108486,0.0002604638,0.000352944,0.002344924,0.0003572529,0.03601479,0.8722118,0.03571239,0.04279233,0.0001929858],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1439587,0.05951101,0.7809126,0.007568632,0.0004113665,0.0008412222,0.000974966,0.001606768,0.00421483],"genre_scores_gemma":[0.3563722,0.03206706,0.6046132,0.002866401,0.0003021218,0.0005955418,0.0008361635,0.0001660673,0.002181354],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007544999,"threshold_uncertainty_score":0.03990221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04995304309119984,"score_gpt":0.3603436232204883,"score_spread":0.3103905801292884,"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."}}