Cargando…
SIPGCN: A Novel Deep Learning Model for Predicting Self-Interacting Proteins from Sequence Information Using Graph Convolutional Networks
Protein is the basic organic substance that constitutes the cell and is the material condition for the life activity and the guarantee of the biological function activity. Elucidating the interactions and functions of proteins is a central task in exploring the mysteries of life. As an important pro...
Autores principales: | Wang, Ying, Wang, Lin-Lin, Wong, Leon, Li, Yang, Wang, Lei, You, Zhu-Hong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9313220/ https://www.ncbi.nlm.nih.gov/pubmed/35884848 http://dx.doi.org/10.3390/biomedicines10071543 |
Ejemplares similares
-
Graph convolutional networks fusing motif-structure information
por: Wang, Bin, et al.
Publicado: (2022) -
A Graph Convolutional Network–Based Method for Chemical-Protein Interaction Extraction: Algorithm Development
por: Wang, Erniu, et al.
Publicado: (2020) -
A deep graph convolutional neural network architecture for graph classification
por: Zhou, Yuchen, et al.
Publicado: (2023) -
Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned
por: Berrendorf, Max, et al.
Publicado: (2020) -
Co-embedding of edges and nodes with deep graph convolutional neural networks
por: Zhou, Yuchen, et al.
Publicado: (2023)