{"id":"W2735908543","doi":"10.1101/162685","title":"Consensus on Molecular Subtypes of Ovarian Cancer","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Cancer Research; Ontario Institute for Cancer Research; University of Toronto; Princess Margaret Cancer Centre","funders":"National Cancer Institute; National Institutes of Health; Ontario Institute for Cancer Research; Cancer Research Society; Canadian Institutes of Health Research; Government of Ontario","keywords":"Subtyping; Classifier (UML); Concordance; Ovarian cancer; Machine learning; Artificial intelligence; Computer science; Computational biology; Biology; Data mining; Bioinformatics; Cancer; Genetics","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002735915,0.0004281976,0.0004296016,0.0001003864,0.0001355709,0.00004859603,0.0007772315,0.0008518472,0.00001973835],"category_scores_gemma":[0.0001470468,0.0004502957,0.0002270758,0.00007519004,0.0002904803,0.000001494334,0.0005243511,0.0003898151,0.000009231079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003851379,"about_ca_system_score_gemma":0.000480904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001046345,"about_ca_topic_score_gemma":0.000004759064,"domain_scores_codex":[0.9980738,0.0000977406,0.0003754784,0.0009253526,0.0001782022,0.0003494462],"domain_scores_gemma":[0.9965958,0.00001109851,0.0005636955,0.002305589,0.0003804558,0.0001433805],"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":[0.00005185449,0.00009790385,0.001639584,0.00009202818,0.0002042513,0.00001644532,7.435946e-7,0.00005492946,0.9944996,0.002255775,0.00108064,0.000006308166],"study_design_scores_gemma":[0.0002817767,0.0001164379,0.009950639,0.0001613869,0.0000878934,2.369611e-8,3.241663e-7,0.0000273165,0.9693156,0.00001267026,0.01958053,0.0004653764],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919763,0.002114967,0.003188142,0.0006773879,0.0003669195,0.0007536765,0.0006974095,0.00008842574,0.0001368202],"genre_scores_gemma":[0.9930536,0.0006716133,0.00524394,0.0003177012,0.0002294604,0.0003694931,0.000003367509,0.00009368663,0.00001711439],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02518391,"threshold_uncertainty_score":0.9997949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01297984307372855,"score_gpt":0.2618833998309348,"score_spread":0.2489035567572062,"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."}}