GraphCPIs: A novel graph-based computational model for potential compound-protein interactions.

Chen, Zhan-Heng; Zhao, Bo-Wei; Li, Jian-Qiang; Guo, Zhen-Hao; You, Zhu-Hong

Chen, Zhan-Heng; Zhao, Bo-Wei; Li, Jian-Qiang; Guo, Zhen-Hao; You, Zhu-Hong.

Afiliación

Chen ZH; Department of Clinical Anesthesiology, Faculty of Anesthesiology, Naval Medical University, Shanghai 200433, China.
Zhao BW; The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.
Li JQ; College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China.
Guo ZH; Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Caoan Road 4800, Shanghai 201804, China.
You ZH; School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China.

Mol Ther Nucleic Acids ; 32: 721-728, 2023 Jun 13.

Article en En | MEDLINE | ID: mdl-37251691

Palabras clave

MT: Bioinformatics; XGBoost; compound-protein interactions; computational methods; graph convolutional network; graph representation; network embedding

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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Mol Ther Nucleic Acids Año: 2023 Tipo del documento: Article País de afiliación: China

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