{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003280595,0.0001410364,0.0002106366,0.0000321869,0.0002815077,0.0000723284,0.0005182006,0.0001879692,0.000309401],"category_scores_gemma":[0.0009924778,0.0001334175,0.0001254575,0.00001763974,0.0002115363,0.000003968898,0.0004120667,0.0001019176,0.00007172376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006278012,"about_ca_system_score_gemma":0.0003502491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000832802,"about_ca_topic_score_gemma":0.0001383855,"domain_scores_codex":[0.9989868,0.0000549931,0.0001980182,0.0003824492,0.0001235461,0.0002541407],"domain_scores_gemma":[0.9987769,0.00005028037,0.0001860652,0.0007490829,0.0001061206,0.0001315254],"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":[0.002443969,0.0003004154,0.006395916,0.0000191176,0.0001469861,0.000117896,0.0001313568,0.00003742623,0.2318163,0.001994749,0.2081897,0.5484061],"study_design_scores_gemma":[0.001548134,0.0007806058,0.01458226,0.000008867446,0.00002089604,0.00004693816,0.00004481119,0.000070383,0.01737843,0.0006679583,0.96466,0.0001907195],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8892332,0.0009737814,0.001992412,0.002142167,0.001878656,0.0003354389,0.00007366686,0.00001709705,0.1033536],"genre_scores_gemma":[0.9895586,0.0008633782,0.004564409,0.00142,0.0006687019,0.00004598806,0.00004915111,0.00002912953,0.002800671],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7564703,"threshold_uncertainty_score":0.5440609,"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."}}