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Semi-ssPTM: A Web Server for Species-Specific Lysine Post-Translational Modification Site Prediction by Semi-Supervised Domain Adaptation
- Source :
- IEEE Transactions on Instrumentation and Measurement; 2024, Vol. 73 Issue: 1 p1-10, 10p
- Publication Year :
- 2024
-
Abstract
- Lysine post-translational modification (PTM) is involved in almost all cellular activities, and plays a critical role in protein structure and function. In the past decades, a variety of experimental methods for identifying PTM sites have emerged, which has promoted the development of PTM research. However, the experimental identification method is time-consuming and labor-intensive. Although deep learning has shown great potential in PTM site prediction recently, existing deep learning-based methods have limited effectiveness in predicting species-specific PTM sites. In this article, we propose a semi-supervised domain adaptation method, named Semi-ssPTM, for species-specific lysine PTM site prediction. Semi-ssPTM improves performance by introducing unlabeled PTM data and effectively enhances the generalization ability of species-specific prediction models. In addition, we train and test predictive models on two common lysine PTM acetylation and ubiquitination datasets. The experimental results show that our proposed semi-supervised method can effectively improve the prediction performance of multiple species and is superior to existing lysine PTM site prediction tools. The web server of Semi-ssPTM, as well as all datasets and associated code used in this study, is freely available at (<uri>http://bmi.ahu.edu.cn/Semi-ssPTM/</uri>). We expect Semi-ssPTM will provide practical guidance and useful insights for PTM site predictions and inspire future bioinformatics research in related fields.
Details
- Language :
- English
- ISSN :
- 00189456 and 15579662
- Volume :
- 73
- Issue :
- 1
- Database :
- Supplemental Index
- Journal :
- IEEE Transactions on Instrumentation and Measurement
- Publication Type :
- Periodical
- Accession number :
- ejs66946061
- Full Text :
- https://doi.org/10.1109/TIM.2024.3420366