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Volume 19, Issue 10
  • ISSN: 1574-8936
  • E-ISSN: 2212-392X

Abstract

Background

With the increasing development of biotechnology, many cancer solutions have been proposed nowadays. In recent years, Neo-peptides-based methods have made significant contributions, with an essential prerequisite of bindings between peptides and HLA molecules. However, the binding is hard to predict, and the accuracy is expected to improve further.

Methods

Therefore, we propose the Crossed Feature Correction Network (CFCN) with deep learning method, which can automatically extract and adaptively learn the discriminative features in HLA-peptide binding, in order to make more accurate predictions on HLA-peptide binding tasks. With the fancy structure of encoding and feature extracting process for peptides, as well as the feature fusion process between fine-grained and coarse-grained level, it shows many advantages on given tasks.

Results

The experiment illustrates that CFCN achieves better performances overall, compared with other fancy models in many aspects.

Conclusion

In addition, we also consider to use multi-view learning methods for the feature fusion process, in order to find out further relations among binding features. Eventually, we encapsulate our model as a useful tool for further research on binding tasks.

© 2024 Bentham Science Publishers
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/content/journals/cbio/10.2174/0115748936299044240202100019
2024-02-16
2024-11-22

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/content/journals/cbio/10.2174/0115748936299044240202100019
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CFCN: An HLA-peptide Prediction Model based on Taylor Extension Theory and Multi-view Learning
Curr Bioinform 19, 977 (2024); https://doi.org/10.2174/0115748936299044240202100019
/content/journals/cbio/10.2174/0115748936299044240202100019
/content/journals/cbio/10.2174/0115748936299044240202100019
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  • Article Type:     Research Article
Keyword(s): biotechnology; HLA molecules; HLA-peptide; leukocyte; multi-view learning; taylor extension theory

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