SIPGCN: A Novel Deep Learning Model for Predicting Self-Interacting Proteins from Sequence Information Using Graph Convolutional Networks.

Wang, Ying; Wang, Lin-Lin; Wong, Leon; Li, Yang; Wang, Lei; You, Zhu-Hong

Wang, Ying; Wang, Lin-Lin; Wong, Leon; Li, Yang; Wang, Lei; You, Zhu-Hong.

Afiliação

Wang Y; College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China.
Wang LL; College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China.
Wong L; Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China.
Li Y; School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China.
Wang L; College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China.
You ZH; Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China.

Biomedicines ; 10(7)2022 Jun 29.

Article em En | MEDLINE | ID: mdl-35884848

Palavras-chave

graph convolutional networks; proteinprotein interactions; random forest; self-interacting protein

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Biomedicines Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China País de publicação: Suíça

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