{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009019191,0.0001321973,0.0001726168,0.0000907767,0.00004460585,0.00001841063,0.0001353981,0.000120459,0.00002052773],"category_scores_gemma":[0.00001978053,0.0001496433,0.0000642044,0.0001173224,0.0000582488,0.000003144367,0.0001005278,0.00006424485,0.000001522358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001182655,"about_ca_system_score_gemma":0.0004308569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001458859,"about_ca_topic_score_gemma":0.0003780832,"domain_scores_codex":[0.9991876,0.00002926286,0.0002697303,0.0002903209,0.00004640916,0.0001766861],"domain_scores_gemma":[0.9995161,0.00001343824,0.00008572593,0.0002705716,0.00008080574,0.00003331613],"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":[0.00002587086,0.00005168917,0.002914109,0.00002697486,0.00002310111,0.000001081741,0.000102085,0.04119104,0.9534691,0.0002245579,0.0009506685,0.001019727],"study_design_scores_gemma":[0.0004461312,0.00004303408,0.0002056631,0.00001127364,0.00003161255,0.000001155471,0.00005510995,0.01367025,0.9642781,0.0002012008,0.02089552,0.0001609272],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9854174,0.008835874,0.004157839,0.0001923232,0.0004680576,0.0002015945,0.00005554529,0.000002284594,0.0006691072],"genre_scores_gemma":[0.9922919,0.003732572,0.001884536,0.001146198,0.000290292,0.00002362011,0.00004462112,0.00001993897,0.0005663813],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02752079,"threshold_uncertainty_score":0.6102277,"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."}}