{"id":"W2571981357","doi":"10.1039/c6ra25007f","title":"High-throughput metabolomics enables biomarker discovery in prostate cancer","year":2017,"lang":"en","type":"article","venue":"RSC Advances","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Burnaby Hospital; Simon Fraser University","funders":"","keywords":"Metabolomics; Prostate cancer; Throughput; Biomarker discovery; Biomarker; Cancer; Computer science; Computational biology; Medicine; Chemistry; Internal medicine; Bioinformatics; Biology; Proteomics; Telecommunications; Biochemistry","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":[0.0001714085,0.0002035499,0.0002994502,0.00005262348,0.0002508189,0.000115218,0.0003310356,0.00006390425,0.00001529021],"category_scores_gemma":[0.0001458756,0.0001675934,0.00007577271,0.00005881896,0.0001967765,0.00005060701,0.0002860739,0.00007635396,0.000003987194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001834288,"about_ca_system_score_gemma":0.00005314376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003279494,"about_ca_topic_score_gemma":0.001486818,"domain_scores_codex":[0.9988273,0.00002893871,0.000221874,0.0004585426,0.0001101552,0.0003531661],"domain_scores_gemma":[0.9991493,0.00001145155,0.0002077427,0.0005431736,0.00004557595,0.00004279281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005452821,0.0002047892,0.1497111,0.0001119938,0.0003801538,0.00002954793,0.0001033799,0.0003297252,0.7921482,0.007603499,0.002230216,0.04660206],"study_design_scores_gemma":[0.001683304,0.0001264008,0.1092601,0.00004226671,0.00005261308,0.000004834379,0.000188541,0.00002475576,0.311158,0.004538522,0.5723127,0.0006079716],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9543426,0.04229023,0.0002892049,0.000692916,0.0007969518,0.0002049507,0.0001249577,0.000009366656,0.001248802],"genre_scores_gemma":[0.9284421,0.06658744,0.001895498,0.0001278562,0.0002294289,0.00008405655,0.00003530976,0.0000224438,0.002575898],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5700825,"threshold_uncertainty_score":0.6834261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01338687388647342,"score_gpt":0.2910316238075432,"score_spread":0.2776447499210697,"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."}}