Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network

• Published in: PloS one Vol. 15; no. 9; p. e0238915
• Main Authors: Xiao, Ze, Deng, Yue
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 24.09.2020; Public Library of Science (PLoS)
• Subjects: Agglomeration; Algorithms; Architecture; Artificial neural networks; Biological activity; Biological computing; Biology and Life Sciences; Coders; Computer and Information Sciences; Computer applications; Computer science; Embedding; Graphical representations; Informatics; Machine learning; Medicine and Health Sciences; Methods; Neighborhoods; Network topologies; Neural networks; Nodes; Noise levels; Physical Sciences; Predictions; Protein interaction; Protein-protein interactions; Proteins; Research and Analysis Methods; Search engines; Social networks; Topology; China

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...