Thông tin tài nguyên

TỐI ƯU HÓA DỰ ĐOÁN TƯƠNG TÁC PROTEIN-PROTEIN TỪ BIỂU DIỄN NGÔN NGỮ THÔNG QUA CƠ CHẾ CHỌN LỌC ĐẶC TRƯNG ĐA GIAI ĐOẠN VÀ HỌC MÁY XẾP CHỒNG

hue-hujos-tt-8152.pdf · Phiên bản 1 · 20 trang

18
Bản gốc
Trang 19 · Đoạn 35Vị trí nguồn

jos.hueuni.edu.vn Tập 134, Số 2A, 2025 45 13. M. Chen, C. J.-T. Ju, G. Zhou, X. Chen, T. Zhang, K.-W. Chang, C. Zaniolo, and W. Wang, “Multifaceted protein–protein interaction prediction based on siamese residual rcnn,” Bioinformatics, vol. 35, no. 14, pp. i305–i314, 2019. 14. S. Sledzieski, R. Singh, L. Cowen, and B. Berger, “Sequence-based prediction of protein- protein interactions: a structure-aware interpretable deep learning model,” BioRxiv, pp. 2021– 01, 2021. 15. T. Sun, B. Zhou, L. Lai, and J. Pei, “Sequence-based prediction of protein protein interaction using a deep-learning algorithm,” BMC bioinformatics, vol. 18, no. 1, p. 277, 2017. 16. K. D. Truong, X. V. Mai, and T. Tri Nguyen, “Predicting protein-protein interactions: A case study using hilbert transform with combining ensemble learning model,” in International Conference on Computational Intelligence in Engineering Science, pp. 53–64, Springer, 2025. 17. Z. Lin, H. Akin, R. Rao, B. Hie, Z. Zhu, W. Lu, N. Smetanin, R. Verkuil, O. Kabeli, Y. Shmueli, et al., “Evolutionary-scale prediction of atomic-level protein structure with a language model,” Science, vol. 379, no. 6637, pp. 1123–1130, 2023. 18. A. Elnaggar, M. Heinzinger, C. Dallago, G. Rehawi, Y. Wang, L. Jones, T. Gibbs, T. Feher, C. Angerer, M. Steinegger, et al., “Prottrans: Toward understanding the language of life through self-supervised learning,” IEEE transactions on pattern analysis and machine intelligence, vol. 44, no. 10, pp. 7112–7127, 2021.

Trang 19 · Đoạn 36Vị trí nguồn

19. T. H. Dang and T. A. Vu, “xcapt5: protein–protein interaction prediction using deep and wide multi- kernel pooling convolutional neural networks with protein language model,” BMC bioinformatics, vol. 25, no. 1, p. 106, 2024. 20. J. Ding, J. Du, H. Wang, and S. Xiao, “A novel two-stage feature selection method based on random forest and improved genetic algorithm for enhancing classification in machine learning,” Scientific Reports, vol. 15, no. 1, p. 16828, 2025. 21. Y. Murakami and K. Mizuguchi, “Recent developments of sequence-based prediction of protein–protein interactions,” Biophysical Reviews, vol. 14, no. 6, pp. 1393–1411, 2022. 22. L. Xian and Y. Wang, “Advances in computational methods for protein–protein interaction prediction,” Electronics, vol. 13, no. 6, p. 1059, 2024. 23. M. Xu, Z. Zhang, J. Lu, Z. Zhu, Y. Zhang, M. Chang, R. Liu, and J. Tang, “Peer: a comprehensive and multi-task benchmark for protein sequence understanding,” Advances in Neural Information Processing Systems, vol. 35, pp. 35156–35173, 2022. 24. X. Du, S. Sun, C. Hu, Y. Yao, Y. Yan, and Y. Zhang, “Deepppi: boosting prediction of protein– protein interactions with deep neural networks,” Journal of chemical information and modeling, vol. 57, no. 6, pp. 1499–1510, 2017. 25. L. Wong, Z.-H. You, S. Li, Y.-A. Huang, and G. Liu, “Detection of protein-protein interactions from amino acid sequences using a rotation forest model with a novel pr-lpq descriptor,” in International conference on intelligent computing, pp. 713–720, Springer, 2015.

Trang 19 · Đoạn 37Vị trí nguồn

26. Y. Wang, Z.-H. You, S. Yang, X. Li, T.-H. Jiang, and X. Zhou, “A high efficient biological language model for predicting protein–protein interactions,” Cells, vol. 8, no. 2, p. 122, 2019. 27. Z.-H. You, L. Zhu, C.-H. Zheng, H.-J. Yu, S.-P. Deng, and Z. Ji, “Prediction of protein- protein interactions from amino acid sequences using a novel multi-scale continuous and discontinuous feature set,” BMC bioinformatics, vol. 15, no. Suppl 15, p. S9, 2014.