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A robust deep learning workflow to predict CD8 + T-cell epitopes.
Lee, Chloe H; Huh, Jaesung; Buckley, Paul R; Jang, Myeongjun; Pinho, Mariana Pereira; Fernandes, Ricardo A; Antanaviciute, Agne; Simmons, Alison; Koohy, Hashem.
Afiliación
  • Lee CH; MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK.
  • Huh J; MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK.
  • Buckley PR; Visual Geometry Group, Department of Engineering Science, University of Oxford, Oxford, OX2 6NN, UK.
  • Jang M; MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK.
  • Pinho MP; MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK.
  • Fernandes RA; Intelligent Systems Lab, Department of Computer Science, University of Oxford, Oxford, OX1 3QG, UK.
  • Antanaviciute A; MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK.
  • Simmons A; Chinese Academy of Medical Sciences (CAMS) Oxford Institute (COI), University of Oxford, Oxford, OX3 7BN, UK.
  • Koohy H; MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK.
Genome Med ; 15(1): 70, 2023 09 13.
Article en En | MEDLINE | ID: mdl-37705109
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo / COVID-19 Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Genome Med Año: 2023 Tipo del documento: Article País de afiliación: Reino Unido
Texto completo
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XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo / COVID-19 Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Genome Med Año: 2023 Tipo del documento: Article País de afiliación: Reino Unido