{"id":"W2886206553","doi":"10.1007/s11306-018-1398-9","title":"Metabolomic identification of diagnostic serum-based biomarkers for advanced stage melanoma","year":2018,"lang":"en","type":"article","venue":"Metabolomics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Metabolomics; Identification (biology); Computational biology; Melanoma; Diagnostic biomarker; Molecular medicine; Biology; Medicine; Biomarker; Cancer research; Bioinformatics; Cancer; Genetics; Cell cycle","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":[0.0006805585,0.0002683376,0.0004518871,0.0001545505,0.0001490101,0.00002383909,0.0003474314,0.0001538807,0.00003881472],"category_scores_gemma":[0.001554353,0.0002574857,0.0002445083,0.0002367472,0.0002853757,0.000009491635,0.00009879676,0.00005729695,0.00001149142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001626189,"about_ca_system_score_gemma":0.0001028686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008512478,"about_ca_topic_score_gemma":0.00003137894,"domain_scores_codex":[0.9982274,0.00007951043,0.0006066646,0.0005535771,0.0001543339,0.0003785381],"domain_scores_gemma":[0.9983132,0.0001620091,0.0004334392,0.0006416778,0.0003693303,0.00008033615],"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":[0.0004116841,0.00009906502,0.002542495,0.00005194761,0.0003531773,2.985919e-7,0.00002317799,0.00006431355,0.9891365,0.00245314,0.0005449569,0.004319308],"study_design_scores_gemma":[0.00153539,0.0003891735,0.01176395,0.000005550716,0.0001792848,0.000001019301,0.00008184512,0.0005385084,0.9061866,0.0002486309,0.07879449,0.0002755995],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9504573,0.003492945,0.0435429,0.0001577704,0.0007776336,0.0006726535,0.0006662802,0.0000221349,0.0002104128],"genre_scores_gemma":[0.9821212,0.0007852122,0.01569031,0.0001742025,0.0002659812,0.0001399554,0.0002114224,0.00004937662,0.0005623475],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08294988,"threshold_uncertainty_score":0.9999877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0110258247678577,"score_gpt":0.2680254145504491,"score_spread":0.2569995897825914,"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."}}