Abstract
The Coronavirus Disease 2019 (COVID-19) pandemic has induced a serious public health threat worldwide. Anti-coronavirus peptides (ACVPs) could act as potential peptide drugs for COVID-19. Computational methods for ACVP identification can improve the development of COVID-19 therapeutic. In this work, we propose PredACVP, which is an ACVPs prediction model using generative adversarial network (GAN)-based data augmentation method and stacked ensemble learning. The GAN-based data augmentation method is utilized to overcome the few-shot learning problem and improve the prediction performance. With the advantage of converting a high-dimensional vector into a low-dimensional vector, the stacked ensemble learning could fuse multi-view information (amino acid composition, dipeptide composition, composition of k-spaced amino acid group pairs, and physicochemical properties) without overfitting. The PredACVP model, which achieves AUC of 0.990 on test datasets, outperforms the state-of-the-art tools for ACVPs identification. PredACVP can improve the prediction performance, and accelerate the development of peptide drugs for COVID-19. GAN used in this work not only can be applied on the data augmentation and peptide for ACVP sequences, but also can be utilized for the other therapeutic peptides.
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Funding
The National Natural Science Foundation of China (62272004); National Key Research and Development Program of China (2020YFA0908700); Anhui Medical University Science Foundation Program by Anhui Medical University (2021xkj169).
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Xu, J., Xu, C., Cao, R., He, Y., Bin, Y., Zheng, CH. (2023). Generative Adversarial Network-Based Data Augmentation Method for Anti-coronavirus Peptides Prediction. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14088. Springer, Singapore. https://doi.org/10.1007/978-981-99-4749-2_6
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