{"id":"W2996481446","doi":"10.3390/cells8121637","title":"Identification and Validation Model for Informative Liquid Biopsy-Based microRNA Biomarkers: Insights from Germ Cell Tumor In Vitro, In Vivo and Patient-Derived Data","year":2019,"lang":"en","type":"article","venue":"Cells","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":104,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network","funders":"Fundação para a Ciência e a Tecnologia; KWF Kankerbestrijding","keywords":"microRNA; Liquid biopsy; Biopsy; Germ cell tumors; In vivo; Biomarker; Germ cell; Medicine; Teratoma; Computational biology; Bioinformatics; Biology; Pathology; Cancer; Internal medicine; Gene","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":[0.00008584566,0.0001231001,0.0001165361,0.00009592943,0.00002302733,0.00003934715,0.0001197728,0.00009409619,0.000003684302],"category_scores_gemma":[0.00002052222,0.000128569,0.00001927401,0.00006602909,0.00003776067,0.00003770029,0.00010465,0.00003294328,0.000003185092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002306799,"about_ca_system_score_gemma":0.00006475027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002695901,"about_ca_topic_score_gemma":0.00001291183,"domain_scores_codex":[0.9991303,0.00004187041,0.0002883799,0.0003584255,0.00006309147,0.0001179155],"domain_scores_gemma":[0.9993169,0.00002451697,0.0001910821,0.0003771812,0.00004707374,0.00004320616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004712703,0.00004820204,0.0001411181,0.00006546642,0.000008919805,3.391718e-7,0.0002709844,0.0003666528,0.9983669,8.854992e-7,0.00009439047,0.0001648962],"study_design_scores_gemma":[0.001070205,0.0000411274,0.000595903,0.00002212954,0.00001031297,2.971904e-7,0.0001044993,0.1069141,0.8908979,0.00003472259,0.0001714265,0.0001374027],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949607,0.0003891736,0.003321139,0.00002156395,0.00005192199,0.0006865693,0.0005459362,0.000004983083,0.00001801448],"genre_scores_gemma":[0.9959918,0.00002457727,0.001775004,0.00007175567,0.00001173711,0.00002767883,0.002073431,0.00001395617,0.00001002093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.107469,"threshold_uncertainty_score":0.5242893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01069719620092596,"score_gpt":0.2272168562490352,"score_spread":0.2165196600481092,"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."}}