{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003429521,0.001295366,0.001792224,0.003562736,0.0006373692,0.002617976,0.001452781,0.003039339,0.003679937],"category_scores_gemma":[0.003887803,0.0004807509,0.001171978,0.003268791,0.002278355,0.004307784,0.001680039,0.006138985,0.001924093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002373142,"about_ca_system_score_gemma":0.003058256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00121517,"about_ca_topic_score_gemma":0.001196673,"domain_scores_codex":[0.9986499,0.0003456477,0.0001340149,0.0002252351,0.0005499718,0.0000952166],"domain_scores_gemma":[0.9960676,0.002553932,0.0002539982,0.0001496301,0.0007695978,0.000205265],"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":[0.0000671624,0.00007656666,0.000295285,0.01612453,0.0001680538,0.0003032876,0.0002028932,0.0008604932,0.00196847,0.05364053,0.06959096,0.8567018],"study_design_scores_gemma":[0.00001215099,0.00006515673,0.0002829035,0.003044538,0.00007230401,0.0009208814,0.00008470085,0.0002808781,0.0006422269,0.02031405,0.9742467,0.0000335135],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00009307956,0.9932907,0.001262698,0.003167755,0.0008383235,0.000007215932,0.0000209492,0.00002618126,0.001293063],"genre_scores_gemma":[0.001599463,0.9938627,0.001296857,0.001402577,0.001160717,0.00001458082,0.00003422162,0.000005837289,0.000623003],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003679937,"threshold_uncertainty_score":0.01813722,"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."}}