{"id":"W2915160617","doi":"10.1371/journal.pcbi.1006799","title":"Integrated structural variation and point mutation signatures in cancer genomes using correlated topic models","year":2019,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; Genome Canada; Canadian Institutes of Health Research; Cycle for Survival; Michael Smith Health Research BC; Memorial Sloan-Kettering Cancer Center","keywords":"Computational biology; Genome; Biology; Context (archaeology); Genomics; Point mutation; Inference; Genetics; Mutation; Structural variation; Bioinformatics; Computer science; Artificial intelligence; Gene","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":[0.00004159486,0.00009693755,0.0001161337,0.00006749908,0.00002859659,0.00001440636,0.00005054115,0.0001330619,0.00003653765],"category_scores_gemma":[0.0000237854,0.00009286025,0.00001986018,0.0000693439,0.00003083265,0.000006638857,0.00004260029,0.00007581283,0.000001504152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004848797,"about_ca_system_score_gemma":0.0001087899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002042578,"about_ca_topic_score_gemma":0.00007853955,"domain_scores_codex":[0.999394,0.00004177956,0.0001649354,0.0002405007,0.00004196251,0.0001168263],"domain_scores_gemma":[0.9996946,0.00004863362,0.00007134906,0.00006184727,0.00009767788,0.00002590507],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007775435,0.00002022983,0.02283968,0.00001288276,0.00005741992,0.000001263247,0.0002119436,0.8714465,0.1009845,0.002747389,0.00001673358,0.001583771],"study_design_scores_gemma":[0.0007516775,0.000137698,0.02838296,0.0000140507,0.00001402677,0.000005858436,0.00002954069,0.953822,0.001465142,0.01514741,0.00007385646,0.0001557864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937932,0.001487632,0.004123193,0.00008477495,0.0001869675,0.0001976404,0.00006267864,0.000006253129,0.00005765161],"genre_scores_gemma":[0.9960956,0.00009354087,0.002951834,0.0002523143,0.00006199723,0.000008839932,0.0005042048,0.000009104428,0.00002255601],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09951932,"threshold_uncertainty_score":0.3786731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0144515766256267,"score_gpt":0.2559816236513503,"score_spread":0.2415300470257236,"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."}}