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End-to-End Deep Learning Model to Predict and Design Secondary Structure Content of Structural Proteins.
Yu, Chi-Hua; Chen, Wei; Chiang, Yu-Hsuan; Guo, Kai; Martin Moldes, Zaira; Kaplan, David L; Buehler, Markus J.
Afiliação
  • Yu CH; Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.
  • Chen W; Department of Engineering Science, National Cheng Kung University, No.1, University Road, Tainan City 701, Taiwan.
  • Chiang YH; Department of Engineering Science, National Cheng Kung University, No.1, University Road, Tainan City 701, Taiwan.
  • Guo K; Department of Civil Engineering, National Cheng Kung University, No.1, University Road, Tainan City 701, Taiwan.
  • Martin Moldes Z; Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.
  • Kaplan DL; Department of Biomedical Engineering, Tufts University, Medford, Massachusetts 02155, United States.
  • Buehler MJ; Department of Biomedical Engineering, Tufts University, Medford, Massachusetts 02155, United States.
ACS Biomater Sci Eng ; 8(3): 1156-1165, 2022 03 14.
Article em En | MEDLINE | ID: mdl-35129957
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: ACS Biomater Sci Eng Ano de publicação: 2022 Tipo de documento: Article
Texto completo
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Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: ACS Biomater Sci Eng Ano de publicação: 2022 Tipo de documento: Article