MiRNN: An Improved Prediction Model of MicroRNA Precursors Using Gated Recurrent Units

  • Meng Cao
  • Dancheng LiEmail author
  • Zhitao Lin
  • Cheng Niu
  • Chen Ding
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)


MicroRNAs (miRNAs) are small noncoding RNAs that derived from hairpin-forming miRNA precursors (pre-miRNAs) and regulating gene expression at the post-transcriptional level. Many sophisticated computational tools have been developed for miRNA prediction. However, all these existing approaches for predicting miRNA require large amounts of task-specific knowledge in the form of handcrafted features and data pre-processing. In this article, we introduce MiRNN (MiRNN is available at, a novel computational predictor based on bidirectional gated recurrent units (GRUs). Our system is truly end-to-end, requiring no feature engineering or data preprocessing, thus making it applicable to a wide range of sequence classification tasks. Its main purpose is to omit the procedure of feature extraction and to provide accurate prediction by using the high-level features extracted from the bidirectional recurrent neural network. The experimental results show that MiRNN can produce state-of-the-art performance on pre-miRNA prediction task. The overall prediction accuracy of our model on miRBase data sets is 93.70%. In addition, we trained our model on various clade specific dataset and obtained increased accuracy.


MiRNN MicroRNA prediction Deep learning Bidirectional RNN GRUs End-to-end model 


  1. 1.
    Ambros, V.: A hierarchy of regulatory genes controls a larva-to-adult developmental switch in C. elegans. Cell 57(1), 49–57 (1989)CrossRefGoogle Scholar
  2. 2.
    Ruvkun, G.: Glimpses of a tiny RNA world. Science 294(5543), 797–799 (2001)CrossRefGoogle Scholar
  3. 3.
    Witkos, T.M., Koscianska, E., Krzyzosiak, W.J.: Practical aspects of microRNA target prediction. Curr. Mol. Med. 11(2), 93–109 (2011)CrossRefGoogle Scholar
  4. 4.
    Lim, L.P., Lau, N.C., Garrett-Engele, P., et al.: Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 433(7027), 769 (2005)CrossRefGoogle Scholar
  5. 5.
    Carrington, J.C., Ambros, V.: Role of microRNAs in plant and animal development. Science 301(5631), 336–338 (2003)CrossRefGoogle Scholar
  6. 6.
    Suh, M.R., Lee, Y., Kim, J.Y., et al.: Human embryonic stem cells express a unique set of microRNAs. Dev. Biol. 270(2), 488–498 (2004)CrossRefGoogle Scholar
  7. 7.
    Williams, A.H., Liu, N., Van Rooij, E., Olson, E.N.: Microrna control of muscle development and disease. Curr. Opin. Cell Biol. 21(3), 461–469 (2009)CrossRefGoogle Scholar
  8. 8.
    Chen, J.F., Mandel, E.M., Thomson, J.M., et al.: The role of microRNA-1 and microRNA-133 in skeletal muscle proliferation and differentiation. Nat. Genet. 38(2), 228 (2006)CrossRefGoogle Scholar
  9. 9.
    Shivdasani, R.A.: MicroRNAs: regulators of gene expression and cell differentiation. Blood 108(12), 3646–3653 (2006)CrossRefGoogle Scholar
  10. 10.
    Ambros, V.: The functions of animal microRNAs. Nature 431(7006), 350 (2004)CrossRefGoogle Scholar
  11. 11.
    Brennecke, J., Hipfner, D.R., Stark, A., et al.: bantam encodes a developmentally regulated microRNA that controls cell proliferation and regulates the proapoptotic gene hid in Drosophila. Cell 113(1), 25–36 (2003)CrossRefGoogle Scholar
  12. 12.
    Poy, M.N., Eliasson, L., Krutzfeldt, J., et al.: A pancreatic islet-specific microRNA regulates insulin secretion. Nature 432(7014), 226 (2004)CrossRefGoogle Scholar
  13. 13.
    Wilfred, B.R., Wang, W.X., Nelson, P.T.: Energizing miRNA research: a review of the role of miRNAs in lipid metabolism, with a prediction that miR-103/107 regulates human metabolic pathways. Mol. Genet. Metab. 91(3), 209–217 (2007)CrossRefGoogle Scholar
  14. 14.
    Fujii, H., Chiou, T.J., Lin, S.I., et al.: A miRNA involved in phosphate-starvation response in Arabidopsis. Curr. Biol. 15(22), 2038–2043 (2005)CrossRefGoogle Scholar
  15. 15.
    Guy, C.L.: Cold acclimation and freezing stress tolerance: role of protein metabolism. Annu. Rev. Plant Biol. 41(1), 187–223 (1990)CrossRefGoogle Scholar
  16. 16.
    Pfeffer, S., Zavolan, M., Grässer, F.A., et al.: Identification of virus-encoded microRNAs. Science 304(5671), 734–736 (2004)CrossRefGoogle Scholar
  17. 17.
    Nelson, J.A.: Small RNAs and large DNA viruses. N. Engl. J. Med. 357(25), 2630–2632 (2007)CrossRefGoogle Scholar
  18. 18.
    Leclercq, M., Diallo, A.B., Blanchette, M.: Computational prediction of the localization of microRNAs within their pre-miRNA. Nucleic Acids Res. 41(15), 7200–7211 (2013)CrossRefGoogle Scholar
  19. 19.
    Park, S., Min, S., Choi, H., et al.: deepMiRGene: deep neural network based precursor microRNA prediction. arXiv preprint arXiv:1605.00017 (2016)
  20. 20.
    S, G.J.: miRBase.
  21. 21.
    Batuwita, R., Palade, V.: microPred: effective classification of pre-miRNAs for human miRNA gene prediction. Bioinformatics 25(8), 989–995 (2009)CrossRefGoogle Scholar
  22. 22.
    Xuan, P., Guo, M., Liu, X., et al.: PlantMiRNAPred: efficient classification of real and pseudo plant pre-miRNAs. Bioinformatics 27(10), 1368–1376 (2011)CrossRefGoogle Scholar
  23. 23.
    Fujita, P.A., Rhead, B., Zweig, A.S., et al.: The UCSC genome browser database: update 2011. Nucleic Acids Res. 39(suppl_1), D876–D882 (2010)Google Scholar
  24. 24.
    Hinton, G.E., Srivastava, N., Krizhevsky, A., et al.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Meng Cao
    • 1
  • Dancheng Li
    • 1
    Email author
  • Zhitao Lin
    • 1
  • Cheng Niu
    • 1
  • Chen Ding
    • 2
  1. 1.Software CollegeNortheastern UniversityShenyangChina
  2. 2.College of Life and Health ScienceNortheastern UniversityShenyangChina

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