{"id":"W4391752838","doi":"10.21203/rs.3.rs-3914861/v1","title":"Integrating Machine Learning-Enhanced Immunopeptidomics and SARS-CoV-2 Population-Scale Analyses Unveils Novel Antigenic Features for Next-Generation COVID-19 Vaccines","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa; Université de Montréal; Institute for Research in Immunology and Cancer; Montreal Heart Institute; University of Victoria; Mila - Quebec Artificial Intelligence Institute; Centre Hospitalier Universitaire Sainte-Justine","funders":"Fonds de Recherche du Québec - Santé; National Institutes of Health; Genome Canada; Canadian Institutes of Health Research; 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","keywords":"Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Virology; 2019-20 coronavirus outbreak; Population; Scale (ratio); Biology; Coronavirus; Sars virus; Computational biology; Medicine; Geography; Infectious disease (medical specialty); Outbreak; Environmental health; Cartography","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001258584,0.0004417551,0.0004956814,0.0003719323,0.0005709032,0.0007954884,0.0004115317,0.0004841341,0.000006410023],"category_scores_gemma":[0.001193964,0.0003738584,0.000301481,0.0002813053,0.00006643075,0.00002048786,0.001401745,0.001107844,0.000003791811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001224949,"about_ca_system_score_gemma":0.0004150607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001354127,"about_ca_topic_score_gemma":0.001375329,"domain_scores_codex":[0.9975148,0.0001839675,0.0006231038,0.0007921053,0.0003594666,0.0005264975],"domain_scores_gemma":[0.9984266,0.0001098939,0.000234364,0.0006188858,0.0004917833,0.0001184807],"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":[0.0001979509,0.00006354162,0.0006851896,0.001899175,0.0002933569,6.23135e-7,0.0005497092,0.00324211,0.9888046,0.0001684586,0.001751181,0.002344126],"study_design_scores_gemma":[0.002374098,0.001041598,0.001931784,0.0006365678,0.0002195686,0.00004384865,0.002567814,0.1898562,0.7895157,0.001929048,0.008519684,0.001364047],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9583802,0.01967617,0.0187761,0.0005667478,0.0002654793,0.001730252,0.0003800155,0.0000594815,0.0001655603],"genre_scores_gemma":[0.980936,0.003442393,0.005206129,0.0001081165,0.000601964,0.0003303701,0.008728037,0.0000871966,0.0005597768],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1992889,"threshold_uncertainty_score":0.9998713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1486857986511318,"score_gpt":0.4283527455984563,"score_spread":0.2796669469473245,"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."}}