{"id":"W2128381498","doi":"10.1186/s12916-014-0156-8","title":"Towards identification of true cancer biomarkers","year":2014,"lang":"en","type":"article","venue":"BMC Medicine","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Mount Sinai Hospital; University of Toronto","funders":"","keywords":"Medicine; Identification (biology); Cancer; Computational biology; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004669309,0.001022128,0.001486116,0.002183236,0.0003967106,0.002980438,0.001211651,0.002934871,0.002858382],"category_scores_gemma":[0.005898569,0.0007775194,0.000715074,0.0009203777,0.001686617,0.003659125,0.002070188,0.00397817,0.003081034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009769337,"about_ca_system_score_gemma":0.001013032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002110128,"about_ca_topic_score_gemma":0.000260029,"domain_scores_codex":[0.9981521,0.000580798,0.00009705489,0.0004260351,0.0006336667,0.0001103796],"domain_scores_gemma":[0.9970171,0.001328161,0.0003293715,0.000236581,0.0009317998,0.0001570315],"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":[0.0004334202,0.0002380321,0.009359922,0.005140333,0.0002726676,0.0007503078,0.0004391369,0.004314263,0.4556947,0.07651452,0.01695774,0.429885],"study_design_scores_gemma":[0.0001426985,0.001441563,0.006498412,0.001864394,0.0005196102,0.004023307,0.000564412,0.03008094,0.3811727,0.1634711,0.4100026,0.0002183437],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06279783,0.1858081,0.7035015,0.0257952,0.00254417,0.0004747188,0.001523377,0.001482145,0.01607293],"genre_scores_gemma":[0.2185402,0.08664509,0.6625022,0.01302197,0.001614086,0.0009723566,0.001977279,0.000276686,0.01445013],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004669309,"threshold_uncertainty_score":0.02469397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02171962391672176,"score_gpt":0.3275014886354404,"score_spread":0.3057818647187187,"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."}}