{"id":"W4310088506","doi":"10.1126/sciadv.abn0238","title":"Widespread hypertranscription in aggressive human cancers","year":2022,"lang":"en","type":"article","venue":"Science Advances","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital; University Health Network; University of Toronto; SickKids Foundation; Hospital for Sick Children; Ontario Institute for Cancer Research","funders":"National Cancer Institute; National Human Genome Research Institute","keywords":"Phenotype; Disease; Cancer; Biology; Gene; Gene expression profiling; Human genome; Computational biology; Gene expression; Bioinformatics; Genome; Genetics; Medicine; Internal medicine","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":[0.000202549,0.000065732,0.00006247587,0.000073266,0.0003236188,0.00002070003,0.0002957594,0.0000130069,0.00003082483],"category_scores_gemma":[0.00007160252,0.0000699601,0.00002396103,0.0002893239,0.0002515248,0.00001207367,0.0001159918,0.0000644291,0.000001307485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001328664,"about_ca_system_score_gemma":0.0002585575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000176621,"about_ca_topic_score_gemma":0.0007069277,"domain_scores_codex":[0.9991746,0.00001540319,0.0001024405,0.0003080546,0.0001828841,0.0002166349],"domain_scores_gemma":[0.9996775,0.000007470523,0.00007050108,0.0001697585,0.00003055526,0.00004425397],"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":[0.00002264762,0.00001911742,0.004066606,0.000002763013,0.000001291277,0.000005347689,0.0001010695,0.01046013,0.9769466,0.0002847111,0.0001826663,0.007907045],"study_design_scores_gemma":[0.001026627,0.0008918452,0.01080943,0.00002525062,0.00000780343,0.00003138304,0.001749364,0.0001414874,0.7295778,0.002714,0.2525476,0.000477405],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993031,0.004307135,0.00006160774,0.0001068139,0.0005072617,0.0001002953,0.00002400386,0.000004946047,0.001856901],"genre_scores_gemma":[0.9989073,0.0002622131,0.0001754584,0.0003495476,0.000063764,0.00006209814,0.00001682147,0.000005683541,0.0001571252],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2523649,"threshold_uncertainty_score":0.285289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009518632352277344,"score_gpt":0.2785956896219668,"score_spread":0.2690770572696895,"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."}}