Impact of Deep learning-DL in the modern computational biology
Keywords:Deep learning, bioinformatics, machine intelligence, biomedicine
Deep learning (DL) has shown unstable improvement in its application to bioinformatics and has displayed thrillingly promising capacity to mine the complex relationship disguised in immense degree natural and biomedical data. A number of comprehensive reviews have been disseminated on such applications, running from evident level studies with future viewpoints to those primarily filling in as educational activities. These reviews have given a sensational preface to and rule for employments of DL in bioinformatics, covering various kinds of Simulated/Machine intelligence (ML) issues, diverse DL constructions, and extents of natural/biomedical issues. Regardless, by far most of these overviews have focused in on past research, while recurring pattern designs in the principled DL field and viewpoints on their future new developments moreover, logical new applications to science and biomedicine are as yet inadequate. We will focus in on present day DL, the constant examples and future headings of the principled DL field, and conjecture new and huge applications in bioinformatics.
Alshahrani, M., Khan, M. A., Maddouri, O., Kinjo, A. R., Queralt-Rosinach, N., & Hoehndorf, R. (2017). Neuro-symbolic representation learning on biological knowledge graphs. Bioinformatics, 33(17), 2723-2730.
Altae-Tran, H., Ramsundar, B., Pappu, A. S., & Pande, V. (2017). Low data drug discovery with one-shot learning. ACS central science, 3(4), 283-293.
Anand, N., & Huang, P. (2018). Generative modeling for protein structures.
Bocicor, M.-I., Czibula, G., & Czibula, I.-G. (2011). A reinforcement learning approach for solving the fragment assembly problem. Paper presented at the 2011 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing.
Doersch, C. (2016). Tutorial on variational autoencoders. arXiv preprint arXiv:1606.05908.
Fei-Fei, L., Fergus, R., & Perona, P. (2006). One-shot learning of object categories. IEEE transactions on pattern analysis and machine intelligence, 28(4), 594-611.
Finn, C., Abbeel, P., & Levine, S. (2017). Model-agnostic meta-learning for fast adaptation of deep networks. Paper presented at the International Conference on Machine Learning.
Hamilton, W. L., Bajaj, P., Zitnik, M., Jurafsky, D., & Leskovec, J. (2018). Embedding logical queries on knowledge graphs. arXiv preprint arXiv:1806.01445.
Hong, Z., Zeng, X., Wei, L., & Liu, X. (2020). Identifying enhancer–promoter interactions with neural network based on pre-trained DNA vectors and attention mechanism. Bioinformatics, 36(4), 1037-1043.
Hornik, K. (1991). Approximation capabilities of multilayer feedforward networks. Neural networks, 4(2), 251-257.
Hou, H., Gan, T., Yang, Y., Zhu, X., Liu, S., Guo, W., & Hao, J. (2019). Using deep reinforcement learning to speed up collective cell migration. BMC bioinformatics, 20(18), 1-10.
Hu, Y.-J., Lin, S.-C., Lin, Y.-L., Lin, K.-H., & You, S.-N. (2014). A meta-learning approach for B-cell conformational epitope prediction. BMC bioinformatics, 15(1), 1-15.
Hu, Z., Ma, X., Liu, Z., Hovy, E., & Xing, E. (2016). Harnessing deep neural networks with logic rules. arXiv preprint arXiv:1603.06318.
Imrie, F., Bradley, A. R., van der Schaar, M., & Deane, C. M. (2020). Deep generative models for 3D linker design. Journal of chemical information and modeling, 60(4), 1983-1995.
Ingraham, J., Garg, V. K., Barzilay, R., & Jaakkola, T. (2019). Generative models for graph-based protein design.
Joslin, J., Gilligan, J., Anderson, P., Garcia, C., Sharif, O., Hampton, J., . . . Jiang, S. (2018). A fully automated high-throughput flow cytometry screening system enabling phenotypic drug discovery. SLAS Discovery: Advancing Life Sciences R&D, 23(7), 697-707.
Killoran, N., Lee, L. J., Delong, A., Duvenaud, D., & Frey, B. J. (2017). Generating and designing DNA with deep generative models. arXiv preprint arXiv:1712.06148.
Li, Y., Huang, C., Ding, L., Li, Z., Pan, Y., & Gao, X. (2019). Deep learning in bioinformatics: Introduction, application, and perspective in the big data era. Methods, 166, 4-21.
Li, Y., Wang, S., Umarov, R., Xie, B., Fan, M., Li, L., & Gao, X. (2018). DEEPre: sequence-based enzyme EC number prediction by deep learning. Bioinformatics, 34(5), 760-769.
Li, Z., Nguyen, S. P., Xu, D., & Shang, Y. (2017). Protein loop modeling using deep generative adversarial network. Paper presented at the 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI).
Park, S., Min, S., Choi, H.-S., & Yoon, S. (2017). Deep recurrent neural network-based identification of precursor micrornas. Paper presented at the Proceedings of the 31st International Conference on Neural Information Processing Systems.
Socher, R., Ganjoo, M., Sridhar, H., Bastani, O., Manning, C. D., & Ng, A. Y. (2013). Zero-shot learning through cross-modal transfer. arXiv preprint arXiv:1301.3666.
Zou, Z., Tian, S., Gao, X., & Li, Y. (2019). mldeepre: Multi-functional enzyme function prediction with hierarchical multi-label deep learning. Frontiers in genetics, 9, 714.
How to Cite
Copyright (c) 2022 European journal of volunteering and community-based projects
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.