Hybrid gMLP model for interaction prediction of MHC-peptide and TCR.

Zhang, Lichao; Li, Haojin; Zhang, Zhenjiu; Wang, Jinjin; Chen, Gang; Chen, Dong; Shi, Wentao; Jia, Gaozhi; Liu, Mingjun

Zhang, Lichao; Li, Haojin; Zhang, Zhenjiu; Wang, Jinjin; Chen, Gang; Chen, Dong; Shi, Wentao; Jia, Gaozhi; Liu, Mingjun.

Afiliación

Zhang L; School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China.
Li H; School of Software, Shandong University, Jinan, China.
Zhang Z; School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China.
Wang J; School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China.
Chen G; Tianjin Universiity, Tianjin, China.
Chen D; Tianjin Universiity, Tianjin, China.
Shi W; School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China.
Jia G; School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China.
Liu M; School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China.

Front Genet ; 13: 1092822, 2022.

Article en En | MEDLINE | ID: mdl-36685858

Palabras clave

MHC-peptide and TCR interaction; artificial intelligence; deep learning; feature representation; gMLP

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Genet Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

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