{"id":"W3094098516","doi":"10.1038/s41598-020-74482-2","title":"Meta-gene markers predict meningioma recurrence with high accuracy","year":2020,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Meningioma and schwannoma management","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Princess Margaret Cancer Centre; University Health Network; University of Toronto; Ontario Institute for Cancer Research; St. Michael's Hospital","funders":"National Institute for Health and Care Research","keywords":"Meningioma; Cohort; Gene ontology; Logistic regression; Microarray; Gene expression; Computational biology; Bioinformatics; Transcriptome; Gene; Oncology; Medicine; Gene regulatory network; Microarray analysis techniques; Gene expression profiling; Internal medicine; Computer science; Biology; Pathology; Genetics","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":[0.001379346,0.0006360436,0.0009617383,0.001764364,0.0003184804,0.001121606,0.0003823313,0.00043892,0.001152568],"category_scores_gemma":[0.00299682,0.0002291328,0.001554465,0.001227993,0.0001597658,0.0003460645,0.0005624826,0.000654569,0.0004815189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004275171,"about_ca_system_score_gemma":0.0004598833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002690818,"about_ca_topic_score_gemma":0.005094361,"domain_scores_codex":[0.9993555,0.0001735488,0.00004642007,0.0002400683,0.0001120209,0.00007244448],"domain_scores_gemma":[0.998686,0.0006923691,0.0002402917,0.0001614198,0.0001385462,0.00008131765],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007435347,0.00009888499,0.9483795,0.0001026822,0.002273115,0.0001788344,0.00006806872,0.008136658,0.01205853,0.000124068,0.001203631,0.02663257],"study_design_scores_gemma":[0.00005354291,0.0003267902,0.8846125,0.0000428698,0.002515345,0.0006754134,0.0001517726,0.09934181,0.007589624,0.001671877,0.002966968,0.00005156267],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9784949,0.002665154,0.01158297,0.0004822026,0.00005892499,0.00003630759,0.005079015,0.0004453531,0.001155166],"genre_scores_gemma":[0.9931544,0.000274072,0.00314659,0.00006555183,0.00002682896,0.00001955959,0.002951843,0.00003469913,0.0003263543],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002690818,"threshold_uncertainty_score":0.007294714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05685010097179508,"score_gpt":0.2624779508797606,"score_spread":0.2056278499079655,"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."}}