{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006732079,0.0001884261,0.0002518479,0.00006412899,0.0001023738,0.000284385,0.00009675296,0.0001370097,0.00002852588],"category_scores_gemma":[0.0001427185,0.0001564437,0.00004012788,0.0001582944,0.0001370615,0.00002208965,0.0002440981,0.0001229024,9.606923e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001927952,"about_ca_system_score_gemma":0.0002229953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007688508,"about_ca_topic_score_gemma":0.001815299,"domain_scores_codex":[0.9981496,0.00004930278,0.0004477736,0.0009686013,0.0001117803,0.0002728974],"domain_scores_gemma":[0.9988214,0.0000390805,0.0002288875,0.0006861803,0.00009872888,0.0001257471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00005287441,0.00006624245,0.1599061,0.00003108072,0.00007103505,0.00005394793,0.000117083,0.0007576591,0.8313987,0.000002463154,0.004173996,0.003368876],"study_design_scores_gemma":[0.008150719,0.0005282644,0.3320022,0.0007101754,0.0004639864,0.001964235,0.0005704622,0.05645186,0.3064025,0.0004716327,0.2888308,0.003453166],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925631,0.004826508,0.0001691988,0.00006945932,0.001042042,0.0003746404,0.0009408808,0.000007766726,0.00000634839],"genre_scores_gemma":[0.9930446,0.001033309,0.003024954,0.0003019476,0.0001423488,0.00005215346,0.001761996,0.0000261526,0.0006125892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5249962,"threshold_uncertainty_score":0.6379588,"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."}}