{"id":"W3106050147","doi":"10.1101/052910","title":"MetaGxData: Clinically Annotated Breast, Ovarian and Pancreatic Cancer Datasets and their Use in Generating a Multi-Cancer Gene Signature","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"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":"","keywords":"Compendium; Pancreatic cancer; Transcriptome; Metadata; Breast cancer; Identification (biology); Gene signature; Ovarian cancer; Cancer; Oncology; Internal medicine; Computational biology; Bioinformatics; Medicine; Biology; Computer science; Gene; Gene expression; World Wide Web","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":[0.005780754,0.0009904895,0.0009105929,0.004829369,0.0007606492,0.001966464,0.00205901,0.0007822824,0.005238317],"category_scores_gemma":[0.01304117,0.0006902549,0.001900576,0.003823556,0.0005234021,0.0007370173,0.00250743,0.001112756,0.001707708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001042099,"about_ca_system_score_gemma":0.00299261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004111914,"about_ca_topic_score_gemma":0.00729488,"domain_scores_codex":[0.9977347,0.0006319066,0.0002436029,0.00081339,0.000482087,0.00009431795],"domain_scores_gemma":[0.9929194,0.003569566,0.0006370601,0.002068983,0.0005229884,0.0002820956],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004327334,0.0005943427,0.2265349,0.007964355,0.006038975,0.002281157,0.001428807,0.07111538,0.09207237,0.02271494,0.3270051,0.2379224],"study_design_scores_gemma":[0.001728822,0.0007504281,0.227586,0.0010895,0.002093899,0.002905648,0.000829105,0.13851,0.08241472,0.04630389,0.4952921,0.000495885],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1165477,0.001900517,0.1207648,0.001519739,0.0003723711,0.000628364,0.7245931,0.02939759,0.004275576],"genre_scores_gemma":[0.1564685,0.0005548715,0.2114029,0.0005059165,0.00007113203,0.001383634,0.625515,0.002559014,0.001538953],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.005780754,"threshold_uncertainty_score":0.03057194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02466758829876808,"score_gpt":0.2709227144407037,"score_spread":0.2462551261419356,"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."}}