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2000
Volume 21, Issue 1
  • ISSN: 1574-8936
  • E-ISSN: 2212-392X

Abstract

Background

G-protein coupled receptors (GPCRs) represent a large family of membrane proteins, distinguished by their seven-transmembrane helical structures. These receptors play a pivotal role in numerous physiological processes. Nowadays, many researchers have proposed computational methods to identify GPCRs. In the past, we introduced a powerful method, EMCBOW-GPCR, which was designed for this purpose. However, the feature extraction technique employed is susceptible to out-of-vocabulary errors, indicating the potential for enhanced accuracy in GPCR identification.

Methods

To solve the challenges, we propose a novel approach termed GPCR-AFPN. This method leverages the FastText algorithm to effectively extract features from protein sequences. Additionally, it employs a powerful deep neural network as the predictive model to improve prediction accuracy.

Results

To validate the efficacy of the proposed GPCR-AFPN method, we conducted five-fold cross-validation and independent tests, respectively. The experimental results indicate that GPCR-AFPN outperforms existing methods.

Conclusion

Overall, our proposed method, GPCR-AFPN, can improve the accuracy of GPCR identification. For the convenience of researchers interested in applying our latest advancements, a user-friendly webserver for GPCR-AFPN is available at www.lzzzlab.top/gpcrafpn/, and the corresponding code can be downloaded at https://github.com/454170054/GPCR-AFPN.

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2025-01-08
2026-02-16

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/content/journals/cbio/10.2174/0115748936349783250101124112
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A Deep Learning Method for Identifying G-Protein Coupled Receptors Based on a Feature Pyramid Network and Attention Mechanism
Curr Bioinform 21, 69 (2026); https://doi.org/10.2174/0115748936349783250101124112
/content/journals/cbio/10.2174/0115748936349783250101124112
/content/journals/cbio/10.2174/0115748936349783250101124112
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  • Article Type:     Research Article
Keyword(s): deep learning; feature extraction; GPCRs; support vector machine; webserver; word embedding

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