Skip to main content
Log in

Can artificial neural replicators be useful for studying RNA replicators?

  • Original Article
  • Published:
Archives of Virology Aims and scope Submit manuscript

Abstract

Here, I discuss the usefulness of the application of special artificial neural systems – neural replicators – to study viroids – small pathogens that are short replicating RNA sequences. Using special representations of nucleotide sequences in the form of two sequences with binary components – these two sequences are incomplete representations of the same nucleotide sequence – I show that these neural systems of different sizes are replicated in a special way on them. This allows us to extract some useful information about viroids and their structure, motifs, and relationships. This study is only the first attempt to use neural replicators to analyze genetic data.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. PNAS 79:2554–2558

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Ezhov AA, Kalambet YA, Cherny DI (1989) Neuron network for the recognition of E. coli promoters. Stud Biophys 129:183–192

    CAS  Google Scholar 

  3. Steeg EW (1993) Neural networks, adaptive optimization, and RNA secondary structure prediction. In: Hunter L (ed) Artificial intelligence and molecular biology. MIT Press, Cambridge, pp 121–160

    Google Scholar 

  4. Liu Q, Ye X, Zhang Y (2006) A hopfield neural network based algorithm for RNA secondary structure prediction. Comput Sci 1:10–16

    Google Scholar 

  5. Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43:59–69

    Google Scholar 

  6. Chavez-Alvarez R, Chavoya A, Mendez-Vazquez A (2014) Discovery of possible gene relationships through the application of self-organizing maps to DNA microarray databases. PLoS One 9:e93233

    PubMed  PubMed Central  Google Scholar 

  7. Delgado S, Mora F, Mora A, Merelo JJ, Briones C (2015) A novel representation of genomic sequences for taxonomic clustering and visualization by means of self-organizing maps. Bioinformatics 31:736–744

    CAS  PubMed  Google Scholar 

  8. Lapedes A, Barnes C, Burks C, Farber R, Sirotkin K (1989) Application of neural networks and other machine learning algorithms to DNA sequence analysis. In: Bell GI, Marr TG (eds) Computers and DNA, SFI studies in the sciences of complexity, vol 7. Addison-Wesley, Rosewood City, pp 157–182

    Google Scholar 

  9. Wu CH (1997) Artificial neural networks for molecular sequence analysis. Comput Chem 21:231–256

    Google Scholar 

  10. Takasaki S, Kawamura Y, Konagaya A (2006) Selecting effective siRNA sequences by using radial basis function network and decision tree learning. BMC Bioinform 7(Suppl 5):S22

    Google Scholar 

  11. Seo TK (2010) Classification of nucleotide sequences using support vector machines. J Mol Evol 71:250–267

    CAS  PubMed  Google Scholar 

  12. Huang S, Cai N, Pacheco PP, Narrandes S, Wang Y, Xu W (2018) Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genom-Proteom 15(1):41–51

    CAS  Google Scholar 

  13. Zou J, Huss M, Abid A, Mohammadi P, Torkamani A, Telenti A (2018) A primer on deep learning in genomics. Nat Genet 51:12–18

    PubMed  Google Scholar 

  14. Bohr H, Bohr J, Brunak S, Cotterill RMJ, Lautrup B, Nørskov L, Olsen OH, Peterson SB (1988) Protein secondary structure and homology by neural networks. FEBS Lett 241:223–228

    CAS  PubMed  Google Scholar 

  15. Qian N, Sejnowski TJ (1988) Predicting the secondary structure of globular proteins using neural network models. J Mol Biol 202:865–884

    CAS  PubMed  Google Scholar 

  16. Wardah W, Khan MGM, Sharma A, Rashid MA (2019) Protein secondary structure prediction using neural networks and deep learning: a review. Comput Biol Chem 81:1–8

    CAS  PubMed  Google Scholar 

  17. Oubounyt M, Louadi Z, Tayara H, Chong KT (2019) DeePromoter: robust promoter predictor using deep learning. Front Genet 10:286

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Angermueller C, Pärnamaa T, Parts L, Stegle O (2016) Deep learning for computational biology. Mol Syst Biol 12:878

    PubMed  PubMed Central  Google Scholar 

  19. Kelley DR, Snoek J, Rinn JL (2016) Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res 26:990–999

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Zhou J, Troyanskaya OG (2015) Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods 12:931–934

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Ezhov AA, Chechetkin VR (1998) Search of hidden periodicities in noisy symbolic sequences with neural networks. Math Model 10:83–92

    Google Scholar 

  22. Sundaram L, Gao H, Padigepati SR et al (2018) Predicting the clinical impact of human mutation with deep neural networks. Nat Genet 50:1161–1170

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Bellot P, Campos G, Pérez-Enciso M (2018) Can deep learning improve genomic prediction of complex human traits? Genetics 210:809–819

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Zheng J, Wang K (2019) Emerging deep learning methods for single cell RNA-seq data analysis. Quant Biol 7:247–254

    CAS  Google Scholar 

  25. Lin C, Jain S, Kim H, Bar-Joseph Z (2017) Using neural networks for reducing the dimensions of single-cell RNA-Seq data. Nucleic Acids Res 45:e156

    PubMed  PubMed Central  Google Scholar 

  26. Tripathi R, Patel S, Kumari V, Chakraborty P, Varadwaj PK (2016) DeepLNC, a long non-coding RNA prediction tool using deep neural network. Netw Model Anal Health Inform Bioinform 5:21

    Google Scholar 

  27. Yu N, Yu Z, Pan Y (2017) A deep learning method for lincRNA detection using auto-encoder algorithm. BMC Bioinform 18:511

    Google Scholar 

  28. Hill ST, Kuintzle R, Teegarden A, Merrill E, Danaee P, Hendrix DA (2018) A deep recurrent neural network discovers complex biological rules to decipher RNA protein-coding potential. Nucleic Acids Res 46:8105–8113

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Yuan Y, Shi Y, Li C, Kim J, Cai W, Han Z, Feng DD (2016) DeepGene: an advanced cancer type classifier based on deep learning and somatic point mutations. BMC Bioinform 17:476

    Google Scholar 

  30. Yousefi S, Amrollahi F, Amgad M, Dong C et al (2017) Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models. Sci Rep 7:11707

    PubMed  PubMed Central  Google Scholar 

  31. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Flagel L, Brandvain Y, Schrider DR (2018) The unreasonable effectiveness of convolutional neural networks in population genetic inference. Mol Biol Evol 3:220–238

    Google Scholar 

  33. Ezhov AA, Vvedensky VL, Khromov AG, Knizhnikova LA (1991) Self-reproducible neural networks with synchronously changing neuronal threshold. In: Holden AV, Kryukov VI (eds) Neurocomputers and attention II: connectionism and neurocomputers. Manchester University Press, Manchester, pp 523–534

    Google Scholar 

  34. Ezhov AA, Khromov AG, Knizhnikova LA, Vvedensky VL (1991) Self-reproducible networks: classification, antagonistic rules and generalization. Neural Netw World 1:52–57

    Google Scholar 

  35. Fernando C, Szathmáry E, Husbands P (2012) Selectionist and evolutionary approaches to brain function: acritical appraisal. Front Comput Neurosci 6:24

    PubMed  PubMed Central  Google Scholar 

  36. Kitzbichler MG, Smith ML, Christensen SR, Bullmore E (2009) Broadband criticality of human brain network synchronization. PLoS Comput Biol 5:e1000314

    PubMed  PubMed Central  Google Scholar 

  37. Bilotta E, Lafusa A, Pantano P (2003) Is self-replication an embedded characteristic of artificial/living matter? In: Artificial life VIII: proc. of the eighth int. conf. on the simulation and synthesis of living systems. MIT Press, pp 38–48

  38. Ezhov AA, Vvedensky VL (1996) Object generation with neural networks (when spurious memories are useful). Neural Netw 9:1491–1495

    PubMed  Google Scholar 

  39. Lakoff G (1987) Women, fire, and dangerous things: what categories reveal about the mind. University of Chicago press, Chicago

    Google Scholar 

  40. Ezhov AA (1994) Empty classes, predictive and clustering thinking networks. Neural Netw World 4:671–688

    Google Scholar 

  41. Crick F, Mitchison G (1983) The function of dream sleep. Nature 304:111–114

    CAS  PubMed  Google Scholar 

  42. Eigen M (1971) Selforganization of matter and the evolution of biological macromolecules. Die Naturwissenschaften 58:465–523

    CAS  PubMed  Google Scholar 

  43. Ezhov AA (2018) Neural network model of unconscious. In: Huang T, Lv J, Sun C, Tuzikov A (eds) Advances in neural networks – ISNN 2018. Springer, Cham, pp 19–28

    Google Scholar 

  44. Ivica NA, Obermayer B, Campbell GW, Rajamani S, Gerland U, Chen IA (2013) The paradox of dual roles in the RNA world: resolving the conflict between stable folding and templating ability. J Mol Evol 77:55–63

    CAS  PubMed  Google Scholar 

  45. Ezhov AA, Khrennikov AY (2005) Agents with left and right dominant hemispheres and quantum statistics. Phys Rev E 71(016138):1–8

    Google Scholar 

  46. Ezhov AA, Khrennikov AY, Terentyeva SS (2008) Indication of a possible symmetry and its breaking in a many-agent model obeying quantum statistics. Phys Rev E 77(031126):1–12

    Google Scholar 

  47. Tsien J (2007) The memory code. Sci Am 297:52–59

    PubMed  Google Scholar 

  48. Adkar-Purushothama CR, Perreault J (2019) Current overview on viroid-host interactions. Wiley Interdiscip Rev RNA (Sept) e1570:1–21

    Google Scholar 

  49. Zhong X, Archual AJ, Amin AA, Ding B (2008) A genomic map of viroid RNA motifs critical to replication and systemic trafficking. Plant Cell 20:35–47

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Van Regenmortel MHV (2019) Solving the species problem in viral taxonomy: recommendations on non-Latinized binomial species names and on abandoning attempts to assign metagenomic viral sequences to species taxa. Arch Virol 164:2223–2229

    PubMed  Google Scholar 

  51. Gas M-E, Hernándes C, Flores R, Daròs J-A (2007) Processing of nuclear viroids in vivo: an interplay between RNA conformations. PLos Pathog 3:1813–1826

    CAS  Google Scholar 

  52. Owens RA, Sano T, Feldstein PA, Hu Y, Stegerd G (2003) Identification of a novel structural interaction in Columnea latent viroid. Virology 313:604–614

    CAS  PubMed  Google Scholar 

  53. Hill JM, Likiw WJ (2014) Comparing miRNAs and viroids; highly conserved molecular mechanisms for the transmission of genetic information. Front Cell Neurosci 8(A45):1–5

    Google Scholar 

  54. Walker PJ et al (2019) Change to virus taxonomy and the International Code of Virus Classification and Nomenclature ratified by the International Committee on Taxonomy of Viruses. Arch Virol 164:2417–2429

    CAS  PubMed  Google Scholar 

  55. Simmonds P et al (2016) Virus taxonomy in the age of metagenomics. Nat Rev Microbiol 15:161–168

    Google Scholar 

  56. Kovalskaya N, Hammond RW (2014) Molecular biology of viroid-host interactions and disease control strategies. Plant Sci 228:48–60

    CAS  PubMed  Google Scholar 

  57. Flores R, Hernándes C, Martínez de Alba AE, Daròs J-A, Di Serio F (2005) Viroids and viroid-host interactions. Annu Rev Phytopathol 43:117–1139

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

I am very grateful to Professor Marc H. V. van Regenmortel for his support and advice, as well as to Dr. Vladimir R. Chechetkin and Dr. Yakov B. Kazanovich for their careful and critical reading of the manuscript and for their very important suggestions for improving it. I also thank Dmitry A. Mazalov for help in preparing the final version of the manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alexandr A. Ezhov.

Ethics declarations

Conflict of interest

The author declares that he has no conflict of interest.

Ethical approval

This research did not contain studies involving human participants or animals.

Additional information

Handling Editor: Marc H. V. Van Regenmortel.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ezhov, A.A. Can artificial neural replicators be useful for studying RNA replicators?. Arch Virol 165, 2513–2529 (2020). https://doi.org/10.1007/s00705-020-04779-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00705-020-04779-0

Navigation