Identification of membrane protein types via deep residual hypergraph neural network

Jiyun Shen; Yiyi Xia; Yiming Lu; Weizhong Lu; Meiling Qian; Hongjie Wu; Qiming Fu; Jing Chen; Jiyun Shen; Yiyi Xia; Yiming Lu; Weizhong Lu; Meiling Qian; Hongjie Wu; Qiming Fu; Jing Chen

doi:10.3934/mbe.2023894

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 11: 20188-20212. doi: 10.3934/mbe.2023894

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Research article

Identification of membrane protein types via deep residual hypergraph neural network

1.
School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
2.
Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, China
3.
Tianping College of Suzhou University of Science and Technology, Suzhou, China

Academic Editor: Yang Kuang

Received: 03 August 2023 Revised: 01 October 2023 Accepted: 13 October 2023 Published: 06 November 2023

A membrane protein's functions are significantly associated with its type, so it is crucial to identify the types of membrane proteins. Conventional computational methods for identifying the species of membrane proteins tend to ignore two issues: High-order correlation among membrane proteins and the scenarios of multi-modal representations of membrane proteins, which leads to information loss. To tackle those two issues, we proposed a deep residual hypergraph neural network (DRHGNN), which enhances the hypergraph neural network (HGNN) with initial residual and identity mapping in this paper. We carried out extensive experiments on four benchmark datasets of membrane proteins. In the meantime, we compared the DRHGNN with recently developed advanced methods. Experimental results showed the better performance of DRHGNN on the membrane protein classification task on four datasets. Experiments also showed that DRHGNN can handle the over-smoothing issue with the increase of the number of model layers compared with HGNN. The code is available at https://github.com/yunfighting/Identification-of-Membrane-Protein-Types-via-deep-residual-hypergraph-neural-network.
- hypergraph neural network,
- initial residual,
- identity mapping,
- identification,
- membrane protein
Citation: Jiyun Shen, Yiyi Xia, Yiming Lu, Weizhong Lu, Meiling Qian, Hongjie Wu, Qiming Fu, Jing Chen. Identification of membrane protein types via deep residual hypergraph neural network[J]. Mathematical Biosciences and Engineering, 2023, 20(11): 20188-20212. doi: 10.3934/mbe.2023894

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Abstract

A membrane protein's functions are significantly associated with its type, so it is crucial to identify the types of membrane proteins. Conventional computational methods for identifying the species of membrane proteins tend to ignore two issues: High-order correlation among membrane proteins and the scenarios of multi-modal representations of membrane proteins, which leads to information loss. To tackle those two issues, we proposed a deep residual hypergraph neural network (DRHGNN), which enhances the hypergraph neural network (HGNN) with initial residual and identity mapping in this paper. We carried out extensive experiments on four benchmark datasets of membrane proteins. In the meantime, we compared the DRHGNN with recently developed advanced methods. Experimental results showed the better performance of DRHGNN on the membrane protein classification task on four datasets. Experiments also showed that DRHGNN can handle the over-smoothing issue with the increase of the number of model layers compared with HGNN. The code is available at https://github.com/yunfighting/Identification-of-Membrane-Protein-Types-via-deep-residual-hypergraph-neural-network.

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