{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003993486,0.0006100426,0.0008781797,0.001606128,0.0004091723,0.001477877,0.00124718,0.001164036,0.0009844661],"category_scores_gemma":[0.01172732,0.0005597931,0.002275837,0.001600704,0.0007589528,0.001474048,0.001115325,0.001614678,0.0002388353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001078671,"about_ca_system_score_gemma":0.000998527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01007067,"about_ca_topic_score_gemma":0.01516475,"domain_scores_codex":[0.9985237,0.0008230225,0.00007014952,0.0003941373,0.0001083888,0.00008061065],"domain_scores_gemma":[0.9906818,0.007940887,0.0005743714,0.0004504047,0.0002265177,0.0001261242],"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":[0.0001510427,0.0000549902,0.01456892,0.0000895144,0.0003136226,0.0001534597,0.0002560548,0.9204491,0.00230058,0.03130741,0.0009928816,0.02936238],"study_design_scores_gemma":[0.000005758339,0.00000853832,0.0007048338,0.00000391822,0.00001710511,0.00001756627,0.000009936176,0.9859546,0.0001680495,0.01291194,0.000190746,0.000006998718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1247918,0.000418908,0.8722134,0.0005226661,0.00002039032,0.00005150374,0.001073103,0.0004509003,0.0004572088],"genre_scores_gemma":[0.8707964,0.0003791541,0.1249717,0.0001627535,0.00009276776,0.0001531185,0.002047252,0.0001377062,0.001259034],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01007067,"threshold_uncertainty_score":0.02111977,"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."}}