{"id":"W4391175562","doi":"10.1038/s42256-023-00787-2","title":"Variational autoencoder for design of synthetic viral vector serotypes","year":2024,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Virus-based gene therapy research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"CIHR Skin Research Training Centre","keywords":"Autoencoder; Computer science; Capsid; Computational biology; Vector (molecular biology); Epitope; Virology; Artificial intelligence; Deep learning; Biology; Virus; Gene; Antibody; Immunology; Genetics","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":[0.0005697732,0.0005134098,0.0004381859,0.0002594801,0.0001843376,0.0003735609,0.0005654918,0.0006782319,0.001352857],"category_scores_gemma":[0.0007851304,0.0004434532,0.0004654899,0.0001712099,0.0003420452,0.000363554,0.0004071518,0.0006646607,0.0003078193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006772104,"about_ca_system_score_gemma":0.0007293799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004127432,"about_ca_topic_score_gemma":0.004379138,"domain_scores_codex":[0.9998767,0.00003242847,0.000005897823,0.00002716714,0.00003619435,0.00002160814],"domain_scores_gemma":[0.9997335,0.0001442626,0.00002455315,0.0000118735,0.00007319368,0.00001260233],"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":[0.00003996462,0.00002811053,0.0002362211,0.00004586505,0.00001950523,0.00002031913,0.00002139833,0.9415027,0.01029518,0.004240816,0.0005391932,0.04301069],"study_design_scores_gemma":[0.000001290639,0.00001130529,0.00002666411,0.000001657107,0.000001966505,0.000002808734,0.000001418869,0.9984841,0.001040249,0.0002825501,0.0001448864,0.000001245671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02794814,0.0002156464,0.9692389,0.00008019501,0.00003499707,0.00003015886,0.00003751073,0.0002525414,0.002161865],"genre_scores_gemma":[0.7574468,0.0002380491,0.2365676,0.0001078105,0.000024717,0.0001620548,0.0001770084,0.0001169508,0.005158859],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004127432,"threshold_uncertainty_score":0.008206844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01527205586092815,"score_gpt":0.3296407372049388,"score_spread":0.3143686813440106,"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."}}