{"id":"W4207000820","doi":"10.1186/s12859-022-04571-8","title":"Epigenetic landscape of drug responses revealed through large-scale ChIP-seq data analyses","year":2022,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Protein Degradation and Inhibitors","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Bioscience Database Center; Japan Science and Technology Agency; Japan Society for the Promotion of Science; Institute of Genetics; Precursory Research for Embryonic Science and Technology; Kyoto University; Japan Agency for Medical Research and Development","keywords":"Epigenetics; Drug discovery; Computational biology; Transcriptome; Biology; DNA microarray; Drug; Disease; Histone; Bioinformatics; Gene; Genetics; Gene expression; Pharmacology; 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":[],"consensus_categories":[],"category_scores_codex":[0.0005502212,0.0001493212,0.0002033483,0.00007259171,0.0001912808,0.00002155696,0.0006670698,0.00006050953,0.0002154291],"category_scores_gemma":[0.0001846751,0.0001378827,0.00009198349,0.0002299179,0.00005987014,0.00002076612,0.0009138705,0.0001117534,0.00001484614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001036834,"about_ca_system_score_gemma":0.000185605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001195929,"about_ca_topic_score_gemma":0.00003744993,"domain_scores_codex":[0.9985823,0.0001463649,0.0005298571,0.0002115245,0.0002996836,0.0002302374],"domain_scores_gemma":[0.9984329,0.00003215485,0.0002947825,0.001124484,0.00006083634,0.00005488663],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002860823,0.001935835,0.1050431,0.001912906,0.000838484,0.00001009393,0.009219987,0.00778278,0.2087058,0.0008740529,0.6548091,0.006007086],"study_design_scores_gemma":[0.004230208,0.0009510899,0.009365176,0.00006792756,0.000233817,0.00006376681,0.01497043,0.09708653,0.2981887,0.0002633748,0.5733343,0.001244636],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9133467,0.00459544,0.06795339,0.0003404999,0.0005993782,0.0009598786,0.003347591,0.00007577605,0.008781329],"genre_scores_gemma":[0.831214,0.0005724806,0.1554972,0.0009940155,0.0002558198,0.0000714907,0.006783566,0.00005032163,0.004561111],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09567791,"threshold_uncertainty_score":0.5622694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05363702471057392,"score_gpt":0.3247655073235028,"score_spread":0.2711284826129289,"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."}}