{"id":"W2001238824","doi":"10.4161/sysb.25313","title":"Genomic and network analysis to study the origin of ovarian cancer","year":2013,"lang":"en","type":"article","venue":"Systems Biomedicine","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Pediatric Oncology Group","funders":"U.S. Department of Defense","keywords":"Ovarian cancer; Ovarian carcinoma; Serous ovarian cancer; Serous fluid; Computer science; Computational biology; Serous carcinoma; Big data; Profiling (computer programming); Bioinformatics; Data science; Biology; Cancer; Medicine; Data mining; Internal medicine","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":[0.0006136114,0.0002041731,0.000191896,0.001551936,0.0002783898,0.0004376499,0.0002373678,0.0002145841,0.0007912223],"category_scores_gemma":[0.002805515,0.00007485165,0.0002167019,0.001465326,0.000353085,0.0004553806,0.0003548737,0.0003321742,0.0001057241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000504803,"about_ca_system_score_gemma":0.0003996529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001321419,"about_ca_topic_score_gemma":0.001923024,"domain_scores_codex":[0.9995553,0.0002380353,0.00001738272,0.00007837162,0.00007724678,0.00003370204],"domain_scores_gemma":[0.9983693,0.001055159,0.0002546841,0.0001225037,0.0001342561,0.00006417398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008507702,0.000374621,0.3383593,0.0006229189,0.0007023243,0.0005879836,0.0004333138,0.1655881,0.1830881,0.08683512,0.001744919,0.2208125],"study_design_scores_gemma":[0.00005050426,0.0002681618,0.3119909,0.00004884758,0.0003361522,0.0005459529,0.0005537894,0.5268362,0.03618941,0.1139925,0.009142331,0.00004522206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.775611,0.00152533,0.2158402,0.0007483852,0.00003442389,0.00007908104,0.001647635,0.0002489866,0.00426492],"genre_scores_gemma":[0.9555124,0.0004629794,0.04227953,0.00006493639,0.00002765614,0.00005598422,0.001207075,0.00001356055,0.0003759085],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001551936,"threshold_uncertainty_score":0.003662646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01556169891870362,"score_gpt":0.2880544790759192,"score_spread":0.2724927801572156,"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."}}