Title:CFCN: An HLA-peptide Prediction Model based on Taylor Extension Theory and Multi-view Learning
Volume: 19
Issue: 10
Author(s): Bing Rao*, Bing Han, Leyi Wei, Zeyu Zhang, Xinbo Jiang*Balachandran Manavalan*
Affiliation:
- School of Information and Electrical Engineering, Hangzhou City University, Hangzhou, China
- School of Qilu Transportation, Shandong University, Jinan,
China
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University,
Suwon, 16419, Republic of Korea
Keywords:
HLA molecules, HLA-peptide, leukocyte, taylor extension theory, multi-view learning, biotechnology.
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.
