{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01436304,0.0008902187,0.002772977,0.005023785,0.0008912664,0.003017365,0.003137606,0.001294106,0.001702482],"category_scores_gemma":[0.03743624,0.0004402513,0.003288492,0.00295313,0.0005416591,0.001582224,0.001775105,0.00134182,0.0008005297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00172916,"about_ca_system_score_gemma":0.002462771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005675387,"about_ca_topic_score_gemma":0.006186434,"domain_scores_codex":[0.9882091,0.003360901,0.001350287,0.003359471,0.003179737,0.0005404669],"domain_scores_gemma":[0.9773416,0.009100407,0.003401873,0.003494994,0.006151774,0.0005092922],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001922685,0.00020186,0.489105,0.002942301,0.006690116,0.0002711595,0.0004394834,0.04856939,0.00628522,0.005722417,0.02006158,0.4177887],"study_design_scores_gemma":[0.0007779219,0.001019863,0.3954577,0.002347703,0.007477514,0.0020332,0.001418745,0.4323572,0.02945751,0.08629353,0.04106511,0.000293926],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5870382,0.02213439,0.3522481,0.003058445,0.0008152335,0.001437474,0.02028919,0.00232539,0.01065363],"genre_scores_gemma":[0.8899753,0.00159574,0.09131245,0.0006571167,0.0002883718,0.0004755335,0.01415594,0.0002267334,0.00131278],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01436304,"threshold_uncertainty_score":0.07595992,"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."}}