ArticleMolecular therapy. Nucleic acids2023
IPs-GRUAtt: An attention-based bidirectional gated recurrent unit network for predicting phosphorylation sites of SARS-CoV-2 infection.
Article in Molecular therapy. Nucleic acids, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- PhoSARte: identification of SARS-CoV-2 phosphorylation sites using contrastive learning and protein language models.Briefings in bioinformatics · 2026Article
- Integrating WGCNA and machine learning algorithm to identify ACSM5 as a prognostic biomarker and therapeutic target for predicting immunotherapy efficacy in non-small cell lung cancer.Translational cancer research · 2026Article
- Empirical Comparison and Analysis of Artificial Intelligence-Based Methods for Identifying Phosphorylation Sites of SARS-CoV-2 Infection.International journal of molecular sciences · 2024Review
- Securing IoT devices with zero day intrusion detection system using binary snake optimization and attention based bidirectional gated recurrent classifier.Scientific reports · 2024Article
- GBMPhos: A Gating Mechanism and Bi-GRU-Based Method for Identifying Phosphorylation Sites of SARS-CoV-2 Infection.Biology · 2024Article
- H2Opred: a robust and efficient hybrid deep learning model for predicting 2'-O-methylation sites in human RNA.Briefings in bioinformatics · 2023Article
- Advancing the accuracy of SARS-CoV-2 phosphorylation site detection via meta-learning approach.Briefings in bioinformatics · 2023Article
- m5U-GEPred: prediction of RNA 5-methyluridine sites based on sequence-derived and graph embedding features.Frontiers in microbiology · 2023Article
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Authors and funding
4 authors.
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Abstract
The global pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has generated tremendous concern and poses a serious threat to international public health. Phosphorylation is a common post-translational modification affecting many essential cellular processes and is inextricably linked to SARS-CoV-2 infection. Hence, accurate identification of phosphorylation sites will be helpful to understand the mechanisms of SARS-CoV-2 infection and mitigate the ongoing COVID-19 pandemic. In the present study, an attention-based bidirectional gated recurrent unit network, called IPs-GRUAtt, was proposed to identify phosphorylation sites in SARS-CoV-2-infected host cells. Comparative results demonstrated that IPs-GRUAtt surpassed both state-of-the-art machine-learning methods and existing models for identifying phosphorylation sites. Moreover, the attention mechanism made IPs-GRUAtt able to extract the key features from protein sequences. These results demonstrated that the IPs-GRUAtt is a powerful tool for identifying phosphorylation sites. For facilitating its academic use, a freely available online web server for IPs-GRUAtt is provided at http://cbcb.cdutcm.edu.cn/phosphory/.
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