{"id":"W4403572232","doi":"10.1101/2024.10.17.618896","title":"<i>In silico</i> generation of synthetic cancer genomes using generative AI","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University Health Network; University of Toronto; Ontario Institute for Cancer Research","funders":"","keywords":"In silico; Generative grammar; Computational biology; Genome; Computer science; Biology; Artificial intelligence; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006511103,0.0004173255,0.0002394667,0.0002887047,0.0002114093,0.0004753305,0.0007373586,0.0007443871,0.002384637],"category_scores_gemma":[0.003105127,0.0002014835,0.000504756,0.0002468383,0.000607415,0.0003082017,0.0006124022,0.0008247988,0.0002608854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005844716,"about_ca_system_score_gemma":0.0004499515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005965506,"about_ca_topic_score_gemma":0.003983649,"domain_scores_codex":[0.9998012,0.00007019193,0.000006506173,0.00003810141,0.00005842113,0.00002559052],"domain_scores_gemma":[0.9984258,0.001236008,0.00007104992,0.0001162241,0.0001075655,0.00004327285],"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":[0.00001905227,0.00001083928,0.001026345,0.00002323805,0.00001111143,0.0000483383,0.0000246045,0.9900894,0.00117244,0.004319088,0.0005799679,0.002675576],"study_design_scores_gemma":[0.000003462828,0.000004993719,0.00008431855,0.000002287266,0.00000109635,0.000008371037,0.000004742215,0.9974306,0.0008707945,0.001285206,0.0003018728,0.000002284768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3484297,0.0002522154,0.6327132,0.00120029,0.0001943819,0.0001433693,0.001548238,0.002835659,0.01268295],"genre_scores_gemma":[0.9182332,0.00008511564,0.07842869,0.0002606136,0.00001968257,0.0001273392,0.0009543802,0.0001891675,0.001701774],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005965506,"threshold_uncertainty_score":0.01186156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02105395495351833,"score_gpt":0.2538054795961712,"score_spread":0.2327515246426528,"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."}}