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Graph Neural Network for Protein–Protein Interaction Prediction: A Comparative Study
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 cel...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9501426/ https://www.ncbi.nlm.nih.gov/pubmed/36144868 http://dx.doi.org/10.3390/molecules27186135 |
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author | Zhou, Hang Wang, Weikun Jin, Jiayun Zheng, Zengwei Zhou, Binbin |
author_facet | Zhou, Hang Wang, Weikun Jin, Jiayun Zheng, Zengwei Zhou, Binbin |
author_sort | Zhou, Hang |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9501426 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95014262022-09-24 Graph Neural Network for Protein–Protein Interaction Prediction: A Comparative Study Zhou, Hang Wang, Weikun Jin, Jiayun Zheng, Zengwei Zhou, Binbin Molecules Article 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. MDPI 2022-09-19 /pmc/articles/PMC9501426/ /pubmed/36144868 http://dx.doi.org/10.3390/molecules27186135 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhou, Hang Wang, Weikun Jin, Jiayun Zheng, Zengwei Zhou, Binbin Graph Neural Network for Protein–Protein Interaction Prediction: A Comparative Study |
title | Graph Neural Network for Protein–Protein Interaction Prediction: A Comparative Study |
title_full | Graph Neural Network for Protein–Protein Interaction Prediction: A Comparative Study |
title_fullStr | Graph Neural Network for Protein–Protein Interaction Prediction: A Comparative Study |
title_full_unstemmed | Graph Neural Network for Protein–Protein Interaction Prediction: A Comparative Study |
title_short | Graph Neural Network for Protein–Protein Interaction Prediction: A Comparative Study |
title_sort | graph neural network for protein–protein interaction prediction: a comparative study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9501426/ https://www.ncbi.nlm.nih.gov/pubmed/36144868 http://dx.doi.org/10.3390/molecules27186135 |
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