Deep learning pan-specific model for interpretable MHC-I peptide binding prediction with improved attention mechanism.

Jin, Jing; Liu, Zhonghao; Nasiri, Alireza; Cui, Yuxin; Louis, Stephen-Yves; Zhang, Ansi; Zhao, Yong; Hu, Jianjun

Jin, Jing; Liu, Zhonghao; Nasiri, Alireza; Cui, Yuxin; Louis, Stephen-Yves; Zhang, Ansi; Zhao, Yong; Hu, Jianjun.

Afiliación

Jin J; Department of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina, USA.
Liu Z; Department of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina, USA.
Nasiri A; Department of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina, USA.
Cui Y; Department of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina, USA.
Louis SY; Department of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina, USA.
Zhang A; Department of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina, USA.
Zhao Y; Department of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina, USA.
Hu J; Department of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina, USA.

Proteins ; 89(7): 866-883, 2021 07.

Article en En | MEDLINE | ID: mdl-33594723

Asunto(s)

Aprendizaje Profundo; Antígenos HLA-A/química; Péptidos/química; Alelos; Área Bajo la Curva; Benchmarking; Sitios de Unión; Bases de Datos de Proteínas; Conjuntos de Datos como Asunto; Antígenos HLA-A/inmunología; Antígenos HLA-A/metabolismo; Humanos; Péptidos/inmunología; Péptidos/metabolismo; Unión Proteica

Palabras clave

IEDB; MHC peptide binding; attention mechanism; binding affinity prediction; convolutional neural networks

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Texto completo: 1 Colección: 01-internacional Asunto principal: Péptidos / Antígenos HLA-A / Aprendizaje Profundo Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Proteins Asunto de la revista: BIOQUIMICA Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos

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