{"id":"W2811135418","doi":"10.1038/s41467-018-04864-8","title":"Targeting EZH2 reactivates a breast cancer subtype-specific anti-metastatic transcriptional program","year":2018,"lang":"en","type":"article","venue":"Nature Communications","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"Jewish General Hospital; McGill University and Génome Québec Innovation Centre; McGill Genome Centre; McGill University; McGill University Health Centre","funders":"Congressionally Directed Medical Research Programs; Canadian Institutes of Health Research; Canada Excellence Research Chairs, Government of Canada; McGill University; U.S. Department of Defense","keywords":"EZH2; Epigenetics; Epigenomics; Breast cancer; Histone; Cancer research; Reprogramming; Cancer; Biology; Metastasis; Histone methyltransferase; PRC2; DNA methylation; Bioinformatics; Medicine; Gene; Genetics; Gene expression","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":[0.0002011492,0.0001297511,0.0001074435,0.0000501496,0.0003051644,0.00004358747,0.0004782905,0.0001895304,0.00005318852],"category_scores_gemma":[0.00004949823,0.0001233406,0.0000696792,0.0002073251,0.0002190515,0.000008294124,0.0001079359,0.0003214164,0.00001323234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001983495,"about_ca_system_score_gemma":0.00007613751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002208667,"about_ca_topic_score_gemma":0.0001313911,"domain_scores_codex":[0.9991208,0.0001109326,0.0002047547,0.0002453906,0.000130558,0.000187572],"domain_scores_gemma":[0.9986091,0.00003136189,0.0001015572,0.0008080652,0.0003892826,0.00006060018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002705083,0.0002279059,0.009424349,0.000006635672,0.00005776167,1.951345e-7,0.00007603366,0.00001492892,0.9758425,0.001193386,0.002092771,0.0110365],"study_design_scores_gemma":[0.0004323943,0.0001475086,0.1320486,0.00002888626,0.00003870054,0.000006949278,0.00007668934,0.0004898531,0.08958186,0.000254151,0.7765858,0.0003086064],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9363217,0.05465012,0.001248475,0.004061744,0.0004574301,0.0006358038,0.0002744886,0.0000835808,0.002266649],"genre_scores_gemma":[0.9781646,0.006288543,0.01409235,0.0001417955,0.0003405155,0.0001006919,0.000746655,0.00002475379,0.0001000966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8862606,"threshold_uncertainty_score":0.5029684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02322784581562181,"score_gpt":0.3274370353252389,"score_spread":0.3042091895096171,"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."}}