一种基于深度学习的苦味肽精准预测方法

杨学冬; 韩丽君; 王榕; 王宏伟; 王晓

doi:10.12187/2023.03.002

June 2023

Article Contents

YANG Xuedong, HAN Lijun, WANG Rong, et al. An accurate identification method of bitter peptides based on deep learning[J]. Journal of Light Industry, 2023, 38(3): 11-16. doi: 10.12187/2023.03.002

Citation: YANG Xuedong, HAN Lijun, WANG Rong, et al. An accurate identification method of bitter peptides based on deep learning[J]. Journal of Light Industry, 2023, 38(3): 11-16. doi: 10.12187/2023.03.002 shu

An accurate identification method of bitter peptides based on deep learning

1. College of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China;
2. College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China;
3. Henan Key Laboratory of Food Safety Data Intelligence, Zhengzhou 450001, China

Received Date: 2022-12-15
Accepted Date: 2023-02-15

Abstract

Given that wet experimental methods were no longer adequate for the rapid identification of bitter peptides, this paper presented Bitter-Fus, a novel predictive deep learning method incorporating traditional manual features and pre-trained deep features. Firstly, the method automatically extracted deep learning features from peptide sequences using a pre-trained protein sequence language model, then fed the deep learning features into a long short-term memory (LSTM) network for dimensionality reduction to retain the most relevant features. Finally, the reduced-dimensional deep features were fused with the manual features composed of traditional amino acids composition (AAC) method and passed into the feedforward neural network to construct a prediction model. The validation experimental results showed that the prediction method Bitter-Fus obtained an accuracy precision value of 0.902 and a Mathews correlation coefficient value of 0.805 in a 10-fold cross-validation, and an accuracy precision value of 0.930 and a Mathews correlation coefficient value of 0.862 in the independent dataset test, which significantly outperformed the current state-of-the-art bitter peptide prediction methods BERT4Bitter and iBitter-SCM.
- bitter peptide,
- deep learning,
- feature extraction,
- feature fusion
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An accurate identification method of bitter peptides based on deep learning

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