{"id":"W4404829234","doi":"10.1038/s41467-024-54734-9","title":"Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines","year":2024,"lang":"en","type":"article","venue":"Nature Communications","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Institute for Research in Immunology and Cancer; Montreal Heart Institute; Université de Montréal; Mila - Quebec Artificial Intelligence Institute; Centre Hospitalier Universitaire Sainte-Justine","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Genome Canada; Alliance de recherche numérique du Canada; National Institute of Allergy and Infectious Diseases; Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; National Institutes of Health; Yale University; U.S. Department of Health and Human Services","keywords":"Coronavirus disease 2019 (COVID-19); Epitope; Virology; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Computational biology; 2019-20 coronavirus outbreak; Coronavirus Infections; Computer science; Biology; Medicine; Immunology; Antibody; Outbreak; Disease; Infectious disease (medical specialty)","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":[0.000284432,0.0001970063,0.0001750803,0.00008969878,0.0003441071,0.0001917464,0.0009945179,0.000273131,0.000006881504],"category_scores_gemma":[0.0002712082,0.0001745532,0.0001405727,0.0002321553,0.00003014181,0.00001594054,0.0006012262,0.000495019,0.00002344878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004232211,"about_ca_system_score_gemma":0.0001714848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001315659,"about_ca_topic_score_gemma":0.00008179522,"domain_scores_codex":[0.9991,0.0000376928,0.0002995408,0.000263599,0.00007876185,0.0002204547],"domain_scores_gemma":[0.9983046,0.0001201873,0.00007174812,0.00132299,0.00008019919,0.0001002553],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003044536,0.0001940229,0.0001169164,0.0003033801,0.0002223667,2.439225e-7,0.000776061,0.001824645,0.9344277,0.0217075,0.03220413,0.007918557],"study_design_scores_gemma":[0.0006284522,0.0001568219,0.00005627987,0.00001672735,0.00004829209,0.000005490849,0.0002953341,0.002486549,0.05163191,0.0002863109,0.9440817,0.0003061372],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1691457,0.306396,0.4376239,0.03479679,0.002235015,0.007642233,0.001352284,0.0007044369,0.04010361],"genre_scores_gemma":[0.9772159,0.004765688,0.0115254,0.001517232,0.0001298635,0.0003447678,0.002277655,0.00004617144,0.00217735],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9118776,"threshold_uncertainty_score":0.7118072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01450736118906217,"score_gpt":0.2898380775872145,"score_spread":0.2753307163981524,"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."}}