{"id":"W3036409630","doi":"10.2196/19371","title":"Structural Basis for Designing Multiepitope Vaccines Against COVID-19 Infection: In Silico Vaccine Design and Validation","year":2020,"lang":"en","type":"article","venue":"JMIR Bioinformatics and Biotechnology","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Epitope; Virology; In silico; CTL*; Biology; Coronavirus; Adjuvant; Immunogenicity; Proteome; Immune system; Computational biology; Immunology; Antigen; Medicine; CD8; Gene; Coronavirus disease 2019 (COVID-19); Bioinformatics; Genetics; Disease; Infectious disease (medical specialty)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002377638,0.0002441108,0.0002765348,0.0001578773,0.0001711891,0.00007486756,0.0001401907,0.0004240695,0.000003745104],"category_scores_gemma":[0.0003727533,0.0002096457,0.00004999889,0.0002244577,0.00004713116,0.00003743441,0.0001995132,0.0001486479,0.000001276814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002053857,"about_ca_system_score_gemma":0.00006271297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001358385,"about_ca_topic_score_gemma":0.00001053,"domain_scores_codex":[0.9988725,0.00002706594,0.0005110584,0.0002439215,0.00006463687,0.0002807742],"domain_scores_gemma":[0.9993737,0.00004612928,0.0002020724,0.0001951993,0.00004930796,0.0001335753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003037964,0.0002343292,0.07142287,0.005411004,0.0006253403,0.000009288051,0.008186048,0.01469314,0.5943487,0.002933276,0.01048755,0.2886104],"study_design_scores_gemma":[0.007925236,0.003067589,0.007489176,0.00007004331,0.00007254606,0.0001400297,0.002238335,0.674709,0.2764964,0.0004265012,0.02610052,0.001264592],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8850801,0.0003729455,0.1086592,0.004500838,0.00005620835,0.001224285,0.00002192066,0.0000633729,0.00002114474],"genre_scores_gemma":[0.9486897,0.001674193,0.04762013,0.001592921,0.00008084349,0.0001396756,0.0001710669,0.0000222495,0.000009209425],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6600159,"threshold_uncertainty_score":0.8549103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02859994386695318,"score_gpt":0.2676427998991772,"score_spread":0.239042856032224,"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."}}