{"id":"W4388223685","doi":"10.1186/s12863-023-01166-x","title":"3-D chromatin conformation, accessibility, and gene expression profiling of triple-negative breast cancer","year":2023,"lang":"en","type":"article","venue":"BMC Genomic Data","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; Ontario Institute for Cancer Research","funders":"Uppsala Multidisciplinary Center for Advanced Computational Science; Instituto de Salud Carlos III; Govern de les Illes Balears; Science for Life Laboratory; Fundación Francisco Cobos; Vetenskapsrådet; Knut och Alice Wallenbergs Stiftelse; European Commission","keywords":"Triple-negative breast cancer; Chromatin; Epigenetics; Breast cancer; Computational biology; Biology; CRISPR; Gene expression profiling; Cancer research; Gene expression; Profiling (computer programming); Gene; Cancer; Genetics; Computer science","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":[0.0003369192,0.0001291835,0.0001626354,0.00004589,0.00007692505,0.00002397615,0.0005006853,0.00008758367,0.00002496335],"category_scores_gemma":[0.000064972,0.0001197332,0.00002710253,0.0000962041,0.00007577948,0.00001625875,0.001162128,0.0000469758,0.000008250487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000152709,"about_ca_system_score_gemma":0.0001732393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005582802,"about_ca_topic_score_gemma":0.00007259456,"domain_scores_codex":[0.9989833,0.00004058129,0.0003367235,0.0003716799,0.00009288331,0.0001748459],"domain_scores_gemma":[0.998852,0.00001687161,0.0002141682,0.0007915601,0.00006877794,0.00005668042],"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":[0.00005920079,0.00001865258,0.01469494,0.000139579,0.00002677428,2.780004e-7,0.00009864566,0.0001754072,0.9821559,0.00001285419,0.001547436,0.001070362],"study_design_scores_gemma":[0.001983789,0.00008800424,0.2590409,0.00007592342,0.0000520766,0.00003103928,0.0007591557,0.0467601,0.6898356,0.0004008072,0.0005212789,0.0004513207],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942629,0.000271848,0.001234078,0.0000322908,0.0001208895,0.0003011653,0.003670816,0.00001550782,0.00009049674],"genre_scores_gemma":[0.9856435,0.0006113195,0.009723439,0.00002929138,0.0001086042,0.00002391525,0.003707888,0.00002078722,0.0001312606],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2923203,"threshold_uncertainty_score":0.488258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02987020813088768,"score_gpt":0.2971238100062424,"score_spread":0.2672536018753547,"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."}}