Cargando…
Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network
Protein–protein interactions (PPIs) are essential for most biological processes. However, current PPI networks present high levels of noise, sparseness and incompleteness, which limits our ability to understand the cell at the system level from the PPI network. Predicting novel (missing) links in no...
Autores principales: | Xiao, Ze, Deng, Yue |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514053/ https://www.ncbi.nlm.nih.gov/pubmed/32970681 http://dx.doi.org/10.1371/journal.pone.0238915 |
Ejemplares similares
-
Predicting Biomedical Interactions with Higher-Order Graph Convolutional Networks
por: KC, Kishan, et al.
Publicado: (2022) -
Hybrid Low-Order and Higher-Order Graph Convolutional Networks
por: Lei, Fangyuan, et al.
Publicado: (2020) -
Node Classification in Complex Social Graphs via Knowledge-Graph Embeddings and Convolutional Neural Network
por: Molokwu, Bonaventure C., et al.
Publicado: (2020) -
Predicting lncRNA-protein interactions with bipartite graph embedding and deep graph neural networks
por: Ma, Yuzhou, et al.
Publicado: (2023) -
Graph-based prediction of Protein-protein interactions with attributed signed graph embedding
por: Yang, Fang, et al.
Publicado: (2020)