{"id":"W4293427664","doi":"10.3389/fgene.2022.987238","title":"A p53 transcriptional signature in primary and metastatic cancers derived using machine learning","year":2022,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Cancer-related Molecular Pathways","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"Common Fund; NIH Office of the Director; National Human Genome Research Institute; National Cancer Institute; BC Cancer Foundation; National Institutes of Health; Genome British Columbia; Genome Canada","keywords":"Gene; Biology; Transcriptome; Gene signature; Alternative splicing; RNA splicing; Transcriptional regulation; Suppressor; Cancer; Tumor suppressor gene; Transcription factor; Cancer research; Computational biology; Genetics; Gene expression; Messenger RNA; Carcinogenesis; RNA","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001835968,0.0001349656,0.0002790939,0.0002910672,0.00007888196,0.000007753078,0.00006380358,0.00005457771,0.00004895011],"category_scores_gemma":[0.00001984925,0.0001592455,0.00004629562,0.0003679056,0.00006306201,0.00002215258,0.00005101178,0.0007543506,1.606205e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001018313,"about_ca_system_score_gemma":0.000372297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001200384,"about_ca_topic_score_gemma":0.00007554894,"domain_scores_codex":[0.9988471,0.0001322594,0.0002278765,0.000264421,0.0002888852,0.000239473],"domain_scores_gemma":[0.9997315,0.00001224294,0.00005694944,0.0001108306,0.00001802769,0.00007045274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000886067,0.0001570296,0.1103851,0.0002614846,0.000213758,0.0006522612,0.003822723,0.3969046,0.4728469,0.00001501181,0.0006629355,0.01319213],"study_design_scores_gemma":[0.01733962,0.001159778,0.07088523,0.0003268663,0.0006315897,0.0005909544,0.003534087,0.8718891,0.008664217,0.0005710165,0.02342871,0.0009788038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9321087,0.0539959,0.01254386,0.0001709738,0.0005447111,0.0004508832,0.00003974919,0.0000202659,0.0001249392],"genre_scores_gemma":[0.941112,0.001054467,0.05694338,0.0005391659,0.00003203207,0.00004488466,0.0001183059,0.00004834913,0.0001073835],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4749845,"threshold_uncertainty_score":0.6493843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01379449600945344,"score_gpt":0.2313842966710133,"score_spread":0.2175898006615599,"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."}}