{"id":"W2883070899","doi":"10.2174/0929867325666180718164712","title":"Cancer Biomarker Discovery for Precision Medicine: New Progress","year":2018,"lang":"en","type":"review","venue":"Current Medicinal Chemistry","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Cancer; Biomarker discovery; Precision medicine; Biomarker; Medicine; Computational biology; Computer science; Medical physics; Data science; Internal medicine; Biology; Pathology; Proteomics; Genetics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002699285,0.000641177,0.001124734,0.00005009917,0.00009203592,0.00004479647,0.0006428331,0.0005219942,0.0001238969],"category_scores_gemma":[0.0005128593,0.0004828255,0.0004877288,0.0001587002,0.0004020451,0.000004815832,0.0002568387,0.0002653098,0.000007826801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001318748,"about_ca_system_score_gemma":0.001598188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001242389,"about_ca_topic_score_gemma":0.000005616279,"domain_scores_codex":[0.9974435,0.00002693295,0.0007278838,0.0009844749,0.0003318162,0.0004854016],"domain_scores_gemma":[0.9980741,0.000115991,0.0005326568,0.0007401438,0.0001788961,0.0003582264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005755104,0.00004042451,0.000007202912,0.01105905,0.000147499,0.000001522842,0.000009153928,6.934321e-8,0.0002113248,0.000003078207,0.2934984,0.6949647],"study_design_scores_gemma":[0.0007366159,0.000163002,0.000001929857,0.01768268,0.001064917,0.00003035269,0.000008347035,0.000003061912,0.0007893706,0.00004477935,0.9789593,0.0005156947],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00004860884,0.994402,0.0009963327,0.0001117598,0.002811104,0.0008863441,0.0004725601,0.0000159682,0.0002553073],"genre_scores_gemma":[0.00001562543,0.9807869,0.00009729479,0.00004428556,0.01347398,0.0004769308,0.003156903,0.000100673,0.001847422],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.694449,"threshold_uncertainty_score":0.9997624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06673842857917817,"score_gpt":0.4096157975974735,"score_spread":0.3428773690182953,"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."}}