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Graph Neural Network for Protein–Protein Interaction Prediction: A Comparative Study
Authors:Hang Zhou  Weikun Wang  Jiayun Jin  Zengwei Zheng  Binbin Zhou
Affiliation:1.School of Computer and Computing Science, Zhejiang University City College, Hangzhou 310015, China;2.College of Computer Science and Technology, Zhejiang University, Hangzhou 310007, China;3.Industry Brain Institute, Zhejiang University City College, Hangzhou 310015, China
Abstract:Proteins are the fundamental biological macromolecules which underline practically all biological activities. Protein–protein interactions (PPIs), as they are known, are how proteins interact with other proteins in their environment to perform biological functions. Understanding PPIs reveals how cells behave and operate, such as the antigen recognition and signal transduction in the immune system. In the past decades, many computational methods have been developed to predict PPIs automatically, requiring less time and resources than experimental techniques. In this paper, we present a comparative study of various graph neural networks for protein–protein interaction prediction. Five network models are analyzed and compared, including neural networks (NN), graph convolutional neural networks (GCN), graph attention networks (GAT), hyperbolic neural networks (HNN), and hyperbolic graph convolutions (HGCN). By utilizing the protein sequence information, all of these models can predict the interaction between proteins. Fourteen PPI datasets are extracted and utilized to compare the prediction performance of all these methods. The experimental results show that hyperbolic graph neural networks tend to have a better performance than the other methods on the protein-related datasets.
Keywords:graph neural networks, protein–  protein interaction, neural networks
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