{"id":"W2590508915","doi":"10.1200/jco.2012.30.30_suppl.16","title":"The lipid metabolome of kidney cancer.","year":2012,"lang":"en","type":"article","venue":"Journal of Clinical Oncology","topic":"Cancer, Lipids, and Metabolism","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Kidney cancer; False discovery rate; Metabolomics; Liquid chromatography–mass spectrometry; Medicine; Cancer; Metabolome; Multiple comparisons problem; Bonferroni correction; Kidney; Mass spectrometry; Internal medicine; Cancer research; Computational biology; Bioinformatics; Chromatography; Biology; Chemistry; Biochemistry; Statistics; Gene","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002671958,0.0003525129,0.0003657858,0.0008960202,0.0003228148,0.0005207247,0.0001463703,0.0002868325,0.002500653],"category_scores_gemma":[0.0003271704,0.0001056053,0.0003669032,0.001673589,0.0001341524,0.0002438087,0.0004180155,0.0002810026,0.0007973782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004408292,"about_ca_system_score_gemma":0.0006019538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003510769,"about_ca_topic_score_gemma":0.003480391,"domain_scores_codex":[0.9998291,0.00001342352,0.00001129444,0.00005315908,0.00006765315,0.00002526016],"domain_scores_gemma":[0.9998969,0.000015099,0.00002698283,0.000009030742,0.00003644394,0.00001553174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002045022,0.00006937939,0.05309547,0.0007132667,0.0001979112,0.0002681855,0.00008737519,0.0005126304,0.8985679,0.0002013641,0.0009458883,0.04329574],"study_design_scores_gemma":[0.00004357749,0.0007432268,0.5895231,0.00006001713,0.0004196159,0.002107571,0.0001821987,0.002941162,0.3849265,0.0006309895,0.01836385,0.00005818291],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9540014,0.01203198,0.005611778,0.0003288681,0.00006692664,0.000134584,0.0239406,0.0002720152,0.00361192],"genre_scores_gemma":[0.9539181,0.005129184,0.0110049,0.0002345596,0.00003665688,0.0001265691,0.02578093,0.00006548296,0.003703595],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003510769,"threshold_uncertainty_score":0.008365512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06660497525809382,"score_gpt":0.4384453172750612,"score_spread":0.3718403420169674,"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."}}