{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004692574,0.000309279,0.0005460279,0.0001090238,0.0004432306,0.00004598835,0.000566038,0.0003070412,0.00005865329],"category_scores_gemma":[0.0003076474,0.0003175758,0.0002182241,0.0005371757,0.0002749933,0.0003443511,0.0002857817,0.0004604651,0.0000928071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001366663,"about_ca_system_score_gemma":0.0001647141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002787129,"about_ca_topic_score_gemma":0.00002853202,"domain_scores_codex":[0.9969389,0.00006161595,0.001375777,0.0009046689,0.0003036201,0.0004154012],"domain_scores_gemma":[0.9973378,0.000427941,0.0007425261,0.0009977723,0.0003156395,0.0001783073],"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":[0.0002537899,0.0008432975,0.01996115,0.00008597435,0.00008977571,8.036378e-7,0.0002347333,0.0003341088,0.9615258,0.01559562,0.0001680372,0.0009069022],"study_design_scores_gemma":[0.0006853378,0.00007308231,0.001651599,0.00008026435,0.00006071482,0.000002815243,0.0001478229,0.004473257,0.963108,0.02760993,0.001659722,0.0004474426],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4905648,0.00003363201,0.5057955,0.0003773858,0.00001886983,0.001768589,0.001048851,0.0001547561,0.0002375934],"genre_scores_gemma":[0.4879479,0.0001057117,0.505354,0.00006276728,0.000124969,0.005430283,0.0007455035,0.00004556078,0.0001833256],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01830955,"threshold_uncertainty_score":0.9999276,"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."}}