{"id":"W31737416","doi":"10.1385/cp:2:1:5","title":"Proteomic and genomic technologies for biomarker discovery","year":2006,"lang":"en","type":"article","venue":"Clinical Proteomics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Mount Sinai Hospital","funders":"","keywords":"Biomarker discovery; Proteomics; Biomarker; Computational biology; Bioinformatics; Data science; Medicine; Biology; Computer science; Genetics","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":[0.000313054,0.0002277024,0.0003342862,0.00004345283,0.0001875306,0.00008990782,0.0003287251,0.0004022211,0.00001012097],"category_scores_gemma":[0.0002174654,0.0002104065,0.0001680543,0.00007906405,0.0004368031,0.0001578809,0.0002277854,0.0003311434,0.000007635072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005993424,"about_ca_system_score_gemma":0.00005405969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001670913,"about_ca_topic_score_gemma":0.000002924656,"domain_scores_codex":[0.9982777,0.00001043904,0.0007070391,0.0006002125,0.00007169133,0.0003329013],"domain_scores_gemma":[0.9988725,0.0001838725,0.0002807435,0.0005624089,0.00005452814,0.00004600723],"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":[0.0002715588,0.000269456,0.01672759,0.0002097088,0.00003985946,0.000001996671,0.000004702503,0.00001369068,0.9247003,0.03379418,0.001481551,0.02248544],"study_design_scores_gemma":[0.001805405,0.0001119457,0.002698179,0.00008018575,0.00006143246,0.00001862108,0.00003985667,0.002488162,0.5122555,0.4533927,0.02630579,0.0007422552],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7569147,0.0001470853,0.2386513,0.0009405548,0.00003658722,0.001504155,0.0001355855,0.000613639,0.001056369],"genre_scores_gemma":[0.5718663,0.0002127796,0.4239127,0.00006128153,0.0002058022,0.002350476,0.00006529241,0.00006111395,0.001264206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4195985,"threshold_uncertainty_score":0.8580127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03393678040176136,"score_gpt":0.3421906406370843,"score_spread":0.308253860235323,"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."}}