{"id":"W4387764764","doi":"10.1158/0008-5472.can-23-1129","title":"Leveraging Tissue-Specific Enhancer–Target Gene Regulatory Networks Identifies Enhancer Somatic Mutations That Functionally Impact Lung Cancer","year":2023,"lang":"en","type":"article","venue":"Cancer Research","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Optech (Canada)","funders":"National Cancer Institute; Fondazione AIRC per la ricerca sul cancro ETS; Fondazione Regionale per la Ricerca Biomedica; National Human Genome Research Institute; Regione Lombardia; Associazione Italiana per la Ricerca sul Cancro","keywords":"Enhancer; Biology; Enhancer RNAs; Carcinogenesis; Epigenomics; Genetics; Gene; Chromatin; Transcription factor; Computational biology; Gene expression; DNA methylation","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":[0.0002721012,0.0001971991,0.0003141275,0.0006285303,0.0001380557,0.0003783043,0.0002040821,0.0002045625,0.0007367665],"category_scores_gemma":[0.0005129561,0.0001546563,0.000260451,0.0003786949,0.0001537068,0.0001738522,0.000369229,0.000261618,0.0001534795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000190236,"about_ca_system_score_gemma":0.0002205082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007694517,"about_ca_topic_score_gemma":0.002037233,"domain_scores_codex":[0.9998448,0.00002188367,0.000009001211,0.00006825505,0.000031557,0.0000243528],"domain_scores_gemma":[0.9997391,0.00009894391,0.0000871056,0.00003464838,0.00001907817,0.00002113884],"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":[0.0002483059,0.00006370203,0.06871386,0.0001362864,0.0001070806,0.0001902316,0.00008851307,0.003118988,0.9059613,0.0005774767,0.00009877935,0.02069556],"study_design_scores_gemma":[0.00003915119,0.000394803,0.707907,0.00004415762,0.0003470375,0.002033803,0.0002113105,0.04737732,0.2350425,0.002442076,0.00412946,0.00003138179],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9864957,0.0008226502,0.01111518,0.00003654621,0.000004075766,0.00001760668,0.0007611318,0.0001541889,0.0005928305],"genre_scores_gemma":[0.992778,0.0002574146,0.005741403,0.00003077624,0.000003808436,0.00001275135,0.0009404654,0.00002142488,0.0002139716],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007694517,"threshold_uncertainty_score":0.002464771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03827442856187536,"score_gpt":0.3648568118694194,"score_spread":0.3265823833075441,"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."}}