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AG-GATCN: A novel method for predicting essential proteins |
| Peishi Yang(杨培实), Pengli Lu(卢鹏丽)†, and Teng Zhang(张腾) |
| School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China |
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Abstract Essential proteins play an important role in disease diagnosis and drug development. Many methods have been devoted to the essential protein prediction by using some kinds of biological information. However, they either ignore the noise presented in the biological information itself or the noise generated during feature extraction. To overcome these problems, in this paper, we propose a novel method for predicting essential proteins called attention gate-graph attention network and temporal convolutional network (AG-GATCN). In AG-GATCN method, we use improved temporal convolutional network (TCN) to extract features from gene expression sequence. To address the noise in the gene expression sequence itself and the noise generated after the dilated causal convolution, we introduce attention mechanism and gating mechanism in TCN. In addition, we use graph attention network (GAT) to extract protein-protein interaction (PPI) network features, in which we construct the feature matrix by introducing node2vec technique and 7 centrality metrics, and to solve the GAT oversmoothing problem, we introduce gated tanh unit (GTU) in GAT. Finally, two types of features are integrated by us to predict essential proteins. Compared with the existing methods for predicting essential proteins, the experimental results show that AG-GATCN achieves better performance.
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Received: 18 October 2022
Revised: 08 December 2022
Accepted manuscript online: 08 February 2023
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PACS:
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89.75.-k
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(Complex systems)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 11861045, 11361033, and 62162040). |
Corresponding Authors:
Pengli Lu
E-mail: lupengli88@163.com
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Cite this article:
Peishi Yang(杨培实), Pengli Lu(卢鹏丽), and Teng Zhang(张腾) AG-GATCN: A novel method for predicting essential proteins 2023 Chin. Phys. B 32 058902
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