{"id":"W3121973936","doi":"10.1158/0008-5472.can-20-1822","title":"3D Functional Genomics Screens Identify CREBBP as a Targetable Driver in Aggressive Triple-Negative Breast Cancer","year":2021,"lang":"en","type":"article","venue":"Cancer Research","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Centre for the Replacement, Refinement and Reduction of Animals in Research; King's College London; Cancer Research UK; National Institute for Health and Care Research; NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research; BC Cancer Agency; Breast Cancer Now; Medical Research Council; Barts Charity; Pfizer","keywords":"Triple-negative breast cancer; Breast cancer; Cancer; Medicine; Oncology; Internal medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003760097,0.0001977816,0.0002395562,0.0001446191,0.000216969,0.0001155094,0.0002724261,0.000202774,0.002085677],"category_scores_gemma":[0.0004037935,0.0002142634,0.0001076158,0.0005086147,0.0002488764,0.00001314538,0.0004498499,0.0004416067,0.00006524038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004953105,"about_ca_system_score_gemma":0.00300988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004664363,"about_ca_topic_score_gemma":0.009515778,"domain_scores_codex":[0.997701,0.0001693164,0.0002724892,0.0007322186,0.0004380537,0.0006869576],"domain_scores_gemma":[0.9981903,0.00009437516,0.0001066573,0.0004314695,0.0009643735,0.0002128447],"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":[0.001292874,0.0002772397,0.02499947,0.00007939755,0.000334238,0.000312145,0.0003871823,0.004511977,0.8820732,0.0005573721,0.07714067,0.008034244],"study_design_scores_gemma":[0.00566406,0.0002458834,0.1292814,0.0003029104,0.00006063885,0.0001408418,0.001166673,0.000387395,0.6861981,0.002009432,0.1736052,0.0009374255],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9808518,0.01341733,0.0001493661,0.001294467,0.0006274497,0.00040583,0.00104985,0.000008109378,0.002195784],"genre_scores_gemma":[0.9685583,0.02006614,0.0002743483,0.0007718399,0.001486059,0.0005239448,0.0002974683,0.00006718717,0.007954757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1958751,"threshold_uncertainty_score":0.9988266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03677260844504455,"score_gpt":0.362714778354144,"score_spread":0.3259421699090995,"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."}}