Advertisement

Prediction of RNA Structures with Pseudoknots Using Convolutional Neural Network

  • Sixin TangEmail author
  • Shiting Li
  • Jing Chen
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1143)

Abstract

To get better predicted results, we show using convolution neural network inference RNA structure with pseudoknots. First, we expound the deep learning and convolution model and algorithm of the neural network theory, then discuss the key problems for RNA secondary structure prediction using convolution neural network modeling, finally, we design a convolutional neural network to predict RNA structure prediction model and experimental verification of the validity of the model, and the experimental results show that the convolution neural network in the prediction of RNA sequence structure has long-range correlation, can improve the prediction accuracy, especially when RNA sequences with pseudoknots structure.

Keywords

Convolution neural network RNA Structure prediction 

Notes

Acknowledgements

This work was supported by Scientific Research Projects (No. 15C0204) of Hunan Education Department and supported by the Science and Technology Plan Project of Hunan Province (2016TP1020). Application-oriented Special Disciplines, Double First-Class University Project of Hunan Province (Xiangjiaotong [2018] 469).

References

  1. 1.
    Li, Z.Y., Huang, C., Bao, C., et al.: Exon-intron circular RNAs regulate transcription in the nucleus. Nat. Struct. Mol. Biol. 22(2), 256–264 (2015)CrossRefGoogle Scholar
  2. 2.
    Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, Doha, Qatar, pp. 1724–1734 (2014)Google Scholar
  3. 3.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  4. 4.
    Zhao, H., Rosin, P.L., Lai, Y.K.: Automatic semantic style transfer using deep convolutional neural networks and soft masks. Vis. Comput. (2019)Google Scholar
  5. 5.
    Jebara, T.: Discriminative, Generative and Imitative Learning. Massachusetts Institute of Technology, Media Laboratory (2001)Google Scholar
  6. 6.
    Nawrocki, E.P.: Structural RNA Homology Search and Alignment Using Covariance Models. Washington University School of Medicine (2009)Google Scholar
  7. 7.
    Come, E., Oukhellou, L., Denoeux, T.: Learning from partially supervised data using mixture models and belief functions. Pattern Recogn. 42(3), 334–348 (2009)CrossRefGoogle Scholar
  8. 8.
    Tanzera, A., Hofackerab, I.L., Lorenz, R.: RNA modifications in structure prediction—status quo and future challenges. Methods 39(10), 23–38 (2018)Google Scholar
  9. 9.
    Jenkins, A.M., Waterhouse, R.M., Muskavitch, M.A.T.: Long non-coding RNA discovery across the genus anopheles reveals conserved secondary structures within and beyond the Gambiae complex. BMC Genomics 16(1), 337–350 (2015)CrossRefGoogle Scholar
  10. 10.
    Tur, G., Dilek, H.D., Schapire, R.E.: Combining active and semi-supervised learning for spoken language understanding. Speech Commun. 45, 171–186 (2005)CrossRefGoogle Scholar
  11. 11.
    Tang, S., Zhou, Y., Zou, S.: The RNA secondary structure prediction based on the lexicalized stochastic grammar model. Comput. Eng. Sci. 3(31), 128–131 (2009)Google Scholar
  12. 12.
    Griffiths-Jones, S., Bateman, A., Marshall, M., Khanna, A., Eddy, S.R.: Rfam: an RNA family database. Nucleic Acids Res. 31(1), 429–441 (2003)CrossRefGoogle Scholar
  13. 13.
    Kappel, K., Das, R.: Sampling native-like structures of RNA-protein complexes through rosetta folding and docking. Structure 31(4), 139–151 (2018)Google Scholar
  14. 14.
    Bellaousov, S., Mathews, D.H.: ProbKnot: fast prediction of RNA secondary structure including pseudoknots. RNA 16(10), 1870–1880 (2010)CrossRefGoogle Scholar
  15. 15.
    Srivastava, R.K., Greff, K., Schmidhuber, J.: Highway networks. arXiv:1505.00387 (2015)
  16. 16.
    Zhao, H.H., Rosin, P., Lai, Y.K.: Image neural network style transfer with global and local optimization fusion. IEEE Access (2019). Zhao, H.H., Rosin, P., Lai, Y.K., Zheng, J.H., Wang, Y.N.: Adaptive gradient-based block compressive sensing with sparsity for noisy images. Multimed. Tools Appl. (2019)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.College of Computer Science and Technology, Hengyang Normal UniversityHengyangChina

Personalised recommendations