Skip to main content
SpringerLink
Log in

A Long Short-Term Memory Neural Network Used to Predict the Exon–Intron Structure of a Gene

  • MOLECULAR BIOPHYSICS
  • Published:
Biophysics Aims and scope Submit manuscript

Abstract

Several models of long short-term memory (LSTM) neural networks were constructed. Each model was trained on the complete mouse genome to predict the exon–intron structure of a gene. The performance of the neural networks was compared using a test sample and experimental sequencing data obtained using rat brain cell cultures after treatment with spicing inhibitors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.

Similar content being viewed by others

REFERENCES

  1. G. Yeo, D. Holste, and G. Kreiman, Genome Biol. 5 (10), R74 (2004).

    Article  Google Scholar 

  2. E. Furlanis and P. Scheiffele, Annu. Rev. Cell Dev. Biol. 34, 451 (2018).

    Article  Google Scholar 

  3. O. Mauger, F. Lemoine, and P. Scheiffele, Neuron 92 (6), 1266 (2016).

    Article  Google Scholar 

  4. G. Biamonti, A. Amato, E. Belloni, et al., Aging Clin. Exp. Res. (2019). https://doi.org/10.1007/s40520-019-01360-x

  5. E. Furlanis, L. Traunmuller, G. Fucile, and P. Scheiffele, Nat. Neurosci. 22 (10), 1709 (2019).

    Article  Google Scholar 

  6. Louadi Z. et al. Genes 10 (8), 587 (2019).

    Article  Google Scholar 

  7. J. Cheng, T. Y. D. Nguyen, K. J. Cygan, et al., Genome Biol. 20 (1), 48 (2019).

    Article  Google Scholar 

  8. F. A. Gers, J. Schmidhuber, and F. Cummins, in Neural Nets WIRN Vietri-99 (Springer, London, 1999), pp. 133–138.

    Google Scholar 

  9. J. Chung, C. Gulcehre, K. H. Cho, and Y. Bengio, arXi-v, 1412.3555 (2014).

  10. D. P. Kingma and J. Ba, arXiv, 1412.6980 (2014).

  11. O. Mauger, F. Lemoine, and P. Scheiffele, Neuron 92 (6), 1266 (2016).

    Article  Google Scholar 

  12. A. Bankevich, S. Nurk, D. Antipov, et al., J. Comput. Biol. 19 (5), 455 (2012).

    Article  MathSciNet  Google Scholar 

  13. N. Aseyev, A. K. Vinarskaya, M. Roshchin, et al., Front. Cell. Neurosci. 11, 348 (2017).

    Article  Google Scholar 

Download references

Funding

This work was supported by the Russian Science Foundation (project no. 19-74-00141).

Author information

Authors and Affiliations

  1. Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, 117485, Moscow, Russia

    L. A. Uroshlev, N. V. Bal & E. A. Chesnokova

Authors
  1. L. A. Uroshlev
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. V. Bal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. E. A. Chesnokova
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to L. A. Uroshlev.

Ethics declarations

Conflict of interests. The authors declare that they have no conflict of interest.

This work does not contain any studies involving animals or human subjects performed by any of the authors.

Additional information

Translated by T. Tkacheva

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Uroshlev, L.A., Bal, N.V. & Chesnokova, E.A. A Long Short-Term Memory Neural Network Used to Predict the Exon–Intron Structure of a Gene. BIOPHYSICS 65, 574–576 (2020). https://doi.org/10.1134/S0006350920040259

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0006350920040259

Search

Navigation