{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002981635,0.0008042143,0.0008172102,0.0008861738,0.0004783194,0.001391529,0.0006742518,0.001023244,0.001055471],"category_scores_gemma":[0.002140047,0.0003274544,0.0007187428,0.0004853646,0.000724621,0.000639925,0.0006260223,0.001068907,0.001105387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007359904,"about_ca_system_score_gemma":0.001118986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009570459,"about_ca_topic_score_gemma":0.000976851,"domain_scores_codex":[0.9981231,0.0006957702,0.0001421859,0.0003551885,0.0005984376,0.00008537756],"domain_scores_gemma":[0.9981571,0.0006716828,0.0003329205,0.0003761078,0.0003501941,0.0001120395],"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":[0.0004067153,0.0002232883,0.004833006,0.0001574103,0.00003947898,0.0002094344,0.0001056936,0.003806162,0.98137,0.001200052,0.0002508624,0.007397842],"study_design_scores_gemma":[0.00002853479,0.0009185646,0.003740268,0.00003065853,0.0001058868,0.0006914184,0.00009875942,0.02696867,0.9602732,0.0009947512,0.006110017,0.00003934414],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4109828,0.002111368,0.5790809,0.0005661616,0.0001132229,0.0007851685,0.002309566,0.001117026,0.00293371],"genre_scores_gemma":[0.7711688,0.001784853,0.2178856,0.0004448201,0.00003695495,0.001359467,0.004313506,0.0002201343,0.002785901],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002981635,"threshold_uncertainty_score":0.01576859,"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."}}