Description
Title: A Comparative Study of Graph Neural Networks for Protein-Protein Interaction Prediction
Abstract: The primary biological macromolecules that form the basis of nearly all biological processes are proteins. Proteins interact with other proteins in their environment to carry out biological functions, which is known as a protein-protein interaction (PPI). Understanding PPIs reveals how cells behave and function, including immune system functions like antigen recognition and signal transduction. Many computational techniques have been created over the last few decades to predict PPIs automatically, using less time and resources than experimental techniques. In this study, we compare a number of graph neural networks for predicting protein-protein interactions. Five network models—neural networks (NN), graph convolutional neural networks (GCN), graph attention networks (GAT), hyperbolic neural networks (HNN), and hyperbolic graph convolutions—are examined and contrasted (HGCN). All of these models can forecast how proteins will interact by using the protein sequence data. To compare the accuracy of all these prediction methods, 14 PPI datasets are extracted. According to the experimental findings, hyperbolic graph neural networks typically perform better on protein-related datasets than the other methods.
Keywords: graph neural networks; protein–protein interaction; neural networks
Paper Quality: SCOPUS / Web of Science Level Research Paper
Subject: Chemistry
Writer Experience: 20+ Years
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