{"id":"W2902037977","doi":"10.1101/486233","title":"Epigenetic mutational landscape in breast cancer: role of the histone methyltransferase gene KMT2D in triple negative tumors","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto","funders":"Instituto de Salud Carlos III; Ministerio de Economía y Competitividad; CRIS Cancer Foundation; European Commission; Centro de Investigación Biomédica en Red de Cáncer; Universidad de Castilla-La Mancha","keywords":"Epigenetics; Biology; Histone methyltransferase; Methyltransferase; Breast cancer; H3K4me3; Histone; Triple-negative breast cancer; Gene; Genetics; Cancer research; Cancer epigenetics; Histone methylation; Cancer; Gene expression; DNA methylation; Methylation; Promoter","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":[0.0001737277,0.0002040511,0.0002594841,0.0008614389,0.000195067,0.0003268518,0.0001304706,0.0002749885,0.001005147],"category_scores_gemma":[0.0003979478,0.00008446319,0.0002174053,0.0007384482,0.0001814946,0.0001125509,0.0002409032,0.0001741831,0.0001869269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002677714,"about_ca_system_score_gemma":0.0001738437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001166907,"about_ca_topic_score_gemma":0.001571197,"domain_scores_codex":[0.9998641,0.0000130392,0.00001304152,0.00005089923,0.00004059523,0.00001830816],"domain_scores_gemma":[0.9998041,0.0000362532,0.00008546835,0.00001419441,0.00002180051,0.00003826568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001722639,0.00004765308,0.6590812,0.0002628023,0.0001963665,0.001806488,0.0001933072,0.0004963261,0.3206181,0.0001355944,0.0003112619,0.01512827],"study_design_scores_gemma":[0.00001450788,0.0001333747,0.970071,0.00002652071,0.0001964166,0.00435255,0.0001713107,0.001021705,0.02234121,0.0001662175,0.001492893,0.00001227084],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99701,0.001128492,0.0003345206,0.00004716976,0.000004640642,0.000005267782,0.001012894,0.00001504195,0.0004419424],"genre_scores_gemma":[0.998611,0.0001786853,0.0003242904,0.0000186307,0.000003186364,0.000004294347,0.0006885734,0.000004580202,0.0001667213],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001166907,"threshold_uncertainty_score":0.003362536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006999897787879996,"score_gpt":0.2334249935560229,"score_spread":0.2264250957681429,"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."}}