{"id":"W4413102667","doi":"10.1016/j.xgen.2025.100969","title":"In silico generation of synthetic cancer genomes using generative AI","year":2025,"lang":"en","type":"article","venue":"Cell Genomics","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University Health Network; University of Toronto; Ontario Institute for Cancer Research","funders":"Canadian Statistical Sciences Institute; Ontario Genomics; Ontario Institute for Cancer Research","keywords":"In silico; Generative grammar; Computational biology; Genome; Cancer; Computer science; Artificial intelligence; Biology; Genetics; 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":[0.0006852239,0.0005598993,0.0003350635,0.0003776665,0.000255212,0.0006178318,0.0008505657,0.001024779,0.002048965],"category_scores_gemma":[0.004136034,0.0003343847,0.0006811306,0.0003963231,0.0006450487,0.0004248929,0.0007962958,0.00104072,0.0002650309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008171012,"about_ca_system_score_gemma":0.0006428629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006855147,"about_ca_topic_score_gemma":0.007392093,"domain_scores_codex":[0.9997441,0.00008715024,0.000009346954,0.00007176862,0.00005530765,0.00003232815],"domain_scores_gemma":[0.9978611,0.001768171,0.00007308057,0.0001422331,0.00011108,0.00004436722],"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":[0.00003263151,0.00001499192,0.001680925,0.00004092241,0.00001804689,0.00005624831,0.00003359858,0.9876605,0.001032018,0.004654385,0.000862451,0.003913234],"study_design_scores_gemma":[0.000007188658,0.00000771449,0.0001266255,0.000003762992,0.000002238524,0.00001282973,0.000007190984,0.9959164,0.0007947781,0.002634167,0.0004840444,0.000002964728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.451031,0.0007283508,0.5278419,0.001523395,0.0002505274,0.0002286619,0.005106101,0.00301292,0.01027719],"genre_scores_gemma":[0.9042702,0.0001725342,0.088556,0.0003961988,0.00002612925,0.0002203732,0.003936946,0.0002163554,0.002205331],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006855147,"threshold_uncertainty_score":0.01363051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01508289868252278,"score_gpt":0.2742052031876215,"score_spread":0.2591223045050988,"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."}}