{"id":"W3207128141","doi":"10.1186/s13073-021-00970-3","title":"Integrative epigenomic and high-throughput functional enhancer profiling reveals determinants of enhancer heterogeneity in gastric cancer","year":2021,"lang":"en","type":"article","venue":"Genome Medicine","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Montreal Heart Institute","funders":"Cancer Science Institute of Singapore, National University of Singapore; National Research Foundation; Ministry of Education - Singapore; National Medical Research Council; National Research Foundation Singapore; Genome Institute of Singapore; Agency for Science, Technology and Research; Medical Research Council; Duke-NUS Medical School","keywords":"Enhancer; Epigenomics; Biology; Histone; Genetics; Computational biology; Single-nucleotide polymorphism; Chromatin immunoprecipitation; Chromatin; Functional genomics; Gene; Transcription factor; Gene expression; Genomics; Genome; DNA 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002210161,0.0001899272,0.0003721811,0.00006511845,0.00004360518,0.000005306968,0.0001003204,0.0001132048,0.0001096877],"category_scores_gemma":[0.00007130924,0.0001619963,0.00004456083,0.0001571579,0.0001192073,0.000004266793,0.0001323913,0.0001073225,0.000001750087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006553045,"about_ca_system_score_gemma":0.0001769777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001230439,"about_ca_topic_score_gemma":0.001042669,"domain_scores_codex":[0.9986982,0.00006281101,0.0004288805,0.0004402424,0.000122163,0.0002476539],"domain_scores_gemma":[0.999319,0.00001899267,0.0001783826,0.0002574784,0.0001554757,0.0000706713],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000539217,0.00003655473,0.02908046,0.00008214465,0.00004831608,0.00000821244,0.0001807865,0.0001674367,0.9681213,0.0000227637,0.00002813881,0.00216992],"study_design_scores_gemma":[0.001550752,0.0003789353,0.188405,0.0001825373,0.0000564203,0.00005038485,0.0005009291,0.0003035199,0.8075908,0.0003270873,0.0003661015,0.0002875509],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9866583,0.01066339,0.001987003,0.00009357303,0.0002459442,0.000178227,0.00006738525,0.000003258708,0.000102951],"genre_scores_gemma":[0.9932101,0.004346409,0.001396202,0.0001178532,0.0002884458,0.00004691624,0.0001603959,0.0000207797,0.0004128724],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1605306,"threshold_uncertainty_score":0.6606018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01281376269654895,"score_gpt":0.2736546094825726,"score_spread":0.2608408467860237,"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."}}