{"id":"W2615928194","doi":"10.1038/s41698-017-0021-2","title":"Towards personalized tumor markers","year":2017,"lang":"en","type":"article","venue":"npj Precision Oncology","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Sinai Hospital; University of Toronto; University Health Network","funders":"","keywords":"Biomarker; Personalized medicine; Biomarker discovery; Genomics; Precision medicine; Cancer; Computational biology; Cancer biomarkers; Bioinformatics; Medicine; Biology; Internal medicine; Proteomics; Gene; Pathology; Genome; Genetics","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":[0.009878708,0.001063223,0.001607407,0.002194432,0.0008252717,0.005850965,0.001780624,0.004303078,0.008160879],"category_scores_gemma":[0.01655811,0.0007527296,0.001157686,0.001297856,0.004057992,0.007217294,0.00520408,0.01082714,0.005578145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002914666,"about_ca_system_score_gemma":0.003151029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007444049,"about_ca_topic_score_gemma":0.0008623403,"domain_scores_codex":[0.9954156,0.002007433,0.0002411396,0.0008519571,0.001161965,0.0003220136],"domain_scores_gemma":[0.9927294,0.003632484,0.0005888065,0.001159046,0.001352523,0.0005377525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002647731,0.000178958,0.004910896,0.001569253,0.0002938315,0.0004274534,0.0007103455,0.00575618,0.00856457,0.352799,0.09272854,0.5317962],"study_design_scores_gemma":[0.00007798275,0.0001999737,0.001272824,0.0009641334,0.0001850178,0.0006834167,0.0003139819,0.00435424,0.005042409,0.4293436,0.5574825,0.00007973955],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01348054,0.1864795,0.4234332,0.3171284,0.008306889,0.00025318,0.001723423,0.00223162,0.04696321],"genre_scores_gemma":[0.2515577,0.1685648,0.3881382,0.1432136,0.01483606,0.0007633285,0.003158828,0.0008333568,0.02893424],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009878708,"threshold_uncertainty_score":0.05224425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01986147688829063,"score_gpt":0.3266108674703687,"score_spread":0.3067493905820781,"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."}}