{"id":"W2949662834","doi":"10.1038/s41598-019-45165-4","title":"MetaGxData: Clinically Annotated Breast, Ovarian and Pancreatic Cancer Datasets and their Use in Generating a Multi-Cancer Gene Signature","year":2019,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Université Laval; Institute of Cancer Research; Ontario Institute for Cancer Research; Institut universitaire de cardiologie et de pneumologie de Québec; University Health Network; University of Toronto; McMaster University; Princess Margaret Cancer Centre","funders":"Natural Sciences and Engineering Research Council of Canada; National Cancer Institute; Ontario Institute for Cancer Research; National Institutes of Health; Ministero dello Sviluppo Economico; Government of Ontario; Canadian Institutes of Health Research; Cancer Research Society","keywords":"Compendium; Pancreatic cancer; Breast cancer; Transcriptome; Metadata; Identification (biology); Gene signature; Ovarian cancer; Cancer; Oncology; Bioinformatics; Medicine; Computational biology; Internal medicine; Computer science; Biology; Gene; Gene expression; World Wide Web","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":[0.006856767,0.001204185,0.001176183,0.006512937,0.0009473352,0.002224685,0.002723377,0.001041258,0.008371289],"category_scores_gemma":[0.01753274,0.000895768,0.002667481,0.005720689,0.0006301348,0.0009607468,0.003550181,0.001451499,0.002891014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001357193,"about_ca_system_score_gemma":0.004977499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006788129,"about_ca_topic_score_gemma":0.01423208,"domain_scores_codex":[0.9969679,0.0007533165,0.0004676824,0.001056802,0.0006074968,0.0001467997],"domain_scores_gemma":[0.990448,0.004601459,0.001105638,0.002665388,0.0007783127,0.0004012649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004297793,0.0004210938,0.1994638,0.01680045,0.008894956,0.002696351,0.002093308,0.03566062,0.06959671,0.0222053,0.4176303,0.2202393],"study_design_scores_gemma":[0.001261691,0.0004653885,0.1712243,0.001603275,0.002787585,0.002884535,0.0006563255,0.02675715,0.03776453,0.02710476,0.727055,0.0004353983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.03619581,0.001886478,0.05980179,0.0008854742,0.0002772764,0.000541898,0.8819768,0.01485406,0.003580315],"genre_scores_gemma":[0.05855725,0.0007677777,0.09916428,0.0004492447,0.00005344641,0.001764151,0.8362815,0.001811435,0.001150938],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008371289,"threshold_uncertainty_score":0.03626251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01758899510335732,"score_gpt":0.279212213594914,"score_spread":0.2616232184915567,"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."}}