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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

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Mai Xuân Văn và CS. Tập 134, Số 2A, 2025 44 Tài trợ Kết quả nghiên cứu này được tài trợ bởi đề tài cấp Đại học Huế, “Nghiên cứu phương pháp nâng cao hiệu suất trích xuất và chọn lọc đặc trưng từ trình tự protein trên các bộ dữ liệu lớn”, mã số: DHH2024-19-04. Tài liệu tham khảo 1. I. Xenarios, D. W. Rice, L. Salwinski, M. K. Baron, E. M. Marcotte, and D. Eisenberg, “Dip: the database of interacting proteins,” Nucleic acids research, vol. 28, no. 1, pp. 289–291, 2000. 2. Y. Li, Z. Wang, L.-P. Li, Z.-H. You, W.-Z. Huang, X.-K. Zhan, and Y.-B. Wang, “Robust and accurate prediction of protein–protein interactions by exploiting evolutionary information,” Scientific Reports, vol. 11, no. 1, p. 16910, 2021. 3. A. E. Modell, S. L. Blosser, and P. S. Arora, “Systematic targeting of protein–protein interactions,” Trends in pharmacological sciences, vol. 37, no. 8, pp. 702–713, 2016. 4. J. De Las Rivas and C. Fontanillo, “Protein–protein interactions essentials: key concepts to building and analyzing interactome networks,” PLoS computational biology, vol. 6, no. 6, p. e1000807, 2010. 5. Y. Guo, L. Yu, Z. Wen, and M. Li, “Using support vector machine combined with auto covariance to predict protein–protein interactions from protein sequences,” Nucleic acids research, vol. 36, no. 9, pp. 3025–3030, 2008. [6] X.-Y. Pan, Y.-N. Zhang, and H.-B. Shen, “Large-scale prediction of human protein- protein interactions from amino acid sequence based on latent topic features,” Journal of proteome research, vol. 9, no. 10, pp. 4992–5001, 2010.

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6. Z.-H. You, Y.-K. Lei, L. Zhu, J. Xia, and B. Wang, “Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis,” BMC bioinformatics, vol. 14, no. Suppl 8, p. S10, 2013. 7. Y.-A. Huang, Z.-H. You, X. Chen, K. Chan, and X. Luo, “Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding,” BMC bioinformatics, vol. 17, no. 1, p. 184, 2016. 8. C. Chen, Q. Zhang, Q. Ma, and B. Yu, “Lightgbm-ppi: Predicting protein-protein interactions through lightgbm with multi-information fusion,” Chemometrics and Intelligent Laboratory Systems, vol. 191, pp. 54–64, 2019. 9. A. Sharma and B. Singh, “Ae-lgbm: Sequence-based novel approach to detect interacting protein pairs via ensemble of autoencoder and lightgbm,” Computers in Biology and Medicine, vol. 125, p. 103964, 2020. 10. B. Yu, C. Chen, H. Zhou, B. Liu, and Q. Ma, “Gtb-ppi: predict protein–protein interactions based on l1- regularized logistic regression and gradient tree boosting,” Genomics, proteomics & bioinformatics, vol. 18, no. 5, pp. 582–592, 2020. 11. Y. Tian, J. Zhou, and J. Wang, “An ensemble lightgbm framework for robust prediction of protein– protein interactions,” Frontiers in Genetics, vol. 14, p. 1163943, 2023. 12. S. Hashemifar, B. Neyshabur, A. A. Khan, and J. Xu, “Predicting protein–protein interactions through sequence-based deep learning,” Bioinformatics, vol. 34, no. 17, pp. i802–i810, 2018.