{"id":"W4323350175","doi":"10.1101/2023.03.04.531110","title":"ProteinVAE: Variational AutoEncoder for Translational Protein Design","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Virus-based gene therapy research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Capsid; Autoencoder; Computational biology; Computer science; Serotype; Epitope; Virology; Neutralizing antibody; Adenoviridae; Artificial intelligence; Genetic enhancement; Deep learning; Biology; Antibody; Gene; Virus; Genetics","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":[0.0009017096,0.0008911751,0.0008137244,0.0003381125,0.000246783,0.0005651895,0.0009205171,0.001236749,0.001973652],"category_scores_gemma":[0.001333554,0.0006110027,0.000840239,0.0003380998,0.0007002762,0.0005360528,0.0008052518,0.001744387,0.0006721215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001049008,"about_ca_system_score_gemma":0.001150309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00673091,"about_ca_topic_score_gemma":0.006841646,"domain_scores_codex":[0.9997793,0.00006735713,0.00001037814,0.0000531462,0.00005618595,0.00003371683],"domain_scores_gemma":[0.9995561,0.0002572293,0.00003359267,0.00003474182,0.00008797727,0.00003039387],"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":[0.0000394163,0.00001968001,0.0002164109,0.00002908608,0.00002295789,0.00002017574,0.00001149974,0.9738268,0.00186413,0.003359862,0.0006827126,0.0199074],"study_design_scores_gemma":[0.000001216755,0.000004060892,0.000008411539,0.000001147899,7.972415e-7,0.000001313341,5.436756e-7,0.9991387,0.0002158429,0.0005206329,0.0001065473,7.875942e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01878492,0.0004736812,0.9773288,0.0002988005,0.0000578974,0.00004125325,0.0001028817,0.0009845642,0.001927137],"genre_scores_gemma":[0.6632337,0.0004201282,0.3275945,0.0003947528,0.00006246048,0.0002531404,0.0006899343,0.0003384879,0.007012786],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00673091,"threshold_uncertainty_score":0.01338345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04117023816678856,"score_gpt":0.2777875965374765,"score_spread":0.236617358370688,"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."}}