Open Access
BIO Web Conf.
Volume 59, 2023
2023 5th International Conference on Biotechnology and Biomedicine (ICBB 2023)
Article Number 03013
Number of page(s) 6
Section Clinical Trials and Medical Device Monitoring
Published online 08 May 2023
  • Ezzat, A., Zhao, P., Min, W., Li, X., & Kwoh, C. K. 2016. Drug-target interaction prediction with graph regularized matrix factorization. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 14(3), 1–1. DOI: 10.1109/TCBB.2016.2530062 [Google Scholar]
  • Langville, A. N., Meyer, C. D., Albright, R., Cox, J., & Duling, D. 2014. Algorithms, initializations, and convergence for the nonnegative matrix factorization. Eprint Arxiv. doi: [Google Scholar]
  • Luo, Y., Zhao, X., Zhou, J., Yang, J., Zhang, Y., & Kuang, W., et al. 2017. A Network Integration Approach for Drug-Target Interaction Prediction and Computational Drug Repositioning from Heterogeneous Information. Research in Computational Molecular Biology. Springer. [Google Scholar]
  • Mousavian, & Masoudi-Nejad. Drug-target interaction prediction via chemogenomic space: Learning-based methods. DOI: 10.1517/17425255.2014.950222 [Google Scholar]
  • Olayan, R. S., Haitham, A., & Bajic, V. B. 2018. Ddr: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches. Bioinformatics (21), 21. DOI: 10.1093/bioinformatics/btx731 [PubMed] [Google Scholar]
  • Sun, C., Cao, Y., Wei, J. M., & Liu, J. 2021. Autoencoder-based drug-target interaction prediction by preserving the consistency of chemical properties and functions of drugs. Bioinformatics. DOI: 10.1093/bioinformatics/btab384 [Google Scholar]
  • Sun, C., Xuan, P., Zhang, T., & Ye, Y. 2020. Graph convolutional autoencoder and generative adversarial network-based method for predicting drug-target interactions. IEEE/ACM Transactions on Computational Biology and Bioinformatics, PP(99), 1–1. DOI: 10.1016/j.patcog.2021.108095 [Google Scholar]
  • Tapio, P., Antti, A., Sami, P., Sushil, S., Agnieszka, S., & Tang, J., et al. 2015. Toward more realistic drug-target interaction predictions. Briefings in Bioinformatics (2), 325–337. DOI: 10.1093/bib/bbu010 [PubMed] [Google Scholar]
  • Twan, V. L., Nabuurs, S. B., & Elena, M. Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics (21), 3036. DOI: 10.1093/bioinformatics/btr500 [Google Scholar]
  • Xing, Chen, Clarence, C., Yan, Xiaotian, & Zhang, et al. 2016. Drug-target interaction prediction: databases, web servers and computational models. Briefings in bioinformatics. DOI: 10.1093/bib/bbv066 [Google Scholar]

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