التفاصيل البيبلوغرافية
| العنوان: |
Research on antimicrobial peptide prediction model based on deep learning and protein language model. (English) |
| المؤلفون: |
WANG Xiao, WU Zhou, WANG Hongwei, WANG Rong, CHEN Haoran |
| المصدر: |
Journal of Light Industry; Apr2024, Vol. 39 Issue 2, p12-18, 7p |
| مصطلحات موضوعية: |
LANGUAGE models, ANTIMICROBIAL peptides, DEEP learning, PROTEIN models, PREDICTION models |
| مستخلص: |
In response to the need for improving prediction accuracy (ACC) in existing models for Antimicrobial Peptides (AMPs), a novel AMP prediction model called DeepGlap was proposed. This model utilized two protein language models for feature extraction from AMP sequences, followed by fusion of feature vectors. These fused vectors were then input into a deep learning network composed of multiple layers of bidirectional long short-term memory networks (mBi-LSTM), one-dimensional convolutional neural networks (1D-CNN), and attention mechanisms. The model underwent performance evaluation and optimization. Results indicated that the model achieved ACC, the Pearson correlation coefficient (MCC), and the area urder the curve (AUC) values of 0.739, 0.489, and 0.81, respectively, demonstrating superior predictive performance compared to existing AMP prediction models. [ABSTRACT FROM AUTHOR] |
|
Copyright of Journal of Light Industry is the property of Journal of Zhengzhou University of Light Industry, Natural Science Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
