Skip to main content
SpringerLink
Log in
Book cover

International Conference on Intelligent Computing

ICIC 2017: Intelligent Computing Theories and Application pp 823–833Cite as

Prediction and Analysis of Mature microRNA with Flexible Neural Tree Model

  • Conference paper
  • First Online:

  • 2061 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10362))

Abstract

miRNA is a class of small non-coding RNA molecules, length of about 20–24 nucleotides. It combines with mRNA by the principle of complementary base pairing to achieve the objective of cracking or suppressing mRNA, which has the function of gene regulation. Therefore, study on the prediction of miRNA is always the hot topic in bioinformatics. In this paper, we drew on a new method of feature extraction and combined the flexible neural tree (FNT) to predict miRNA. For comparison, we adopted XUE dataset, used the training dataset to train the classifier, and then used the classifier to test on testing dataset. The final average accuracy rate of our experiment that is 93.7% is higher than the prediction method of XUE triple-SVM. So our method achieves a better classification effect.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Bartel, D.P.: MicroRNAs: Genomics, Biogenesis, Mechanism, and Function. Cell 116(2), 281–297 (2004). ISSN 0092-8674

    Article  Google Scholar 

  2. Berezikov, E., Cuppen, E., Plasterk, R.H.A.: Approaches to mi-croRNA discovery. Nat. Genet. 38(Suppl.), S2–S7 (2006)

    Article  Google Scholar 

  3. Kim, V.N., Nam, J.W.: Genomics of microRNA. Trends Genet. 22(3), 165–173 (2006)

    Article  Google Scholar 

  4. Griffiths-Jones, S.: The microRNA registry. Nucleic Acids Res. 32(1), 109–111 (2004)

    Article  Google Scholar 

  5. Karolchik, D., Baertsc, H.R., Diekhans, M., et al.: The UCSC genome browser database. Nucleic Acids Res. 31(1), 51–54 (2003)

    Article  Google Scholar 

  6. Hofacker, I.L.: Vienna RNA secondary structure server. Nucleic Acids Res. 31(13), 3429–3431 (2003)

    Article  Google Scholar 

  7. Pruitt, K.D., Maglot, T.D.R.: RefSeq and LocusLink:NCBI genecentered resources. Nucleic Acids Res. 29(1), 137–140 (2001)

    Article  Google Scholar 

  8. Wang, M.: Identifying pre-miRNA using couplet-syntax for local sequence-structure information. Nanjing University of Aeronautics and Astronautics (2010)

    Google Scholar 

  9. Chen, Y.H., Yang, B., Dong, J.W.: Nonlinear systems modeling via optimal design of neural trees. Int. J. Neural Syst. 14(2), 125–138 (2004)

    Article  MathSciNet  Google Scholar 

  10. Chen, Y.H., Yang, B., Dong, J.W., et al.: Time-series forecasting using flexible neural tree model. Inf. Sci. 174(3), 219–235 (2005)

    Article  MathSciNet  Google Scholar 

  11. Ding, Y.: Computational Intelligence. Science Press, Beingjing (2004)

    Google Scholar 

  12. Salustowicz, R.P., Schmidhuber, J.: Probabilistic incremental program evolution. Evol. Comput. 2(5), 123–141 (1997)

    Article  Google Scholar 

  13. Chen, Y., Kawaji, S.: System identification and control using probabilistic incremental program evolution algorithm. J. Robot. Mechatron. 12(6), 657–681 (2000)

    Article  Google Scholar 

  14. Chen, Y., Kawaji, S.: Evolving ANNs by hybrid approaches of PIPE and random search algorithm for system identification. In: Proceedings of the 3rd Asian Control Conference, 2206–2211 (2000)

    Google Scholar 

  15. Kennedy, J., Eberhart, R.: Partical swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks. [s. I. ]: [s. n. ], pp. 1942–1948 (1995)

    Google Scholar 

  16. Xue, C., Li, F., He, T., et al.: Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. BMC Bioinf. 6, 310–316 (2005)

    Article  Google Scholar 

  17. Griffiths-Jones, S., Grocock, R.J., van Dongen, S., et al.: miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 34(Database issue), D140–D144 (2006)

    Article  Google Scholar 

Download references

Acknowledgment

This research was supported by the National Key Research And Development Program of China (No. 2016YFC0106000), National Natural Science Foundation of China (Grant No. 61302128, 61573166, 61572230, 61671220, 61640218), the Youth Science and Technology Star Program of Jinan City (201406003), the Natural Science Foundation of Shandong Province (ZR2013FL002), the Shandong Distinguished Middle-aged and Young Scientist Encourage and Reward Foundation, China (Grant No. ZR2016FB14), the Project of Shandong Province Higher Educational Science and Technology Program, China (Grant No. J16LN07), the Shandong Province Key Research and Development Program, China (Grant No. 2016GGX101022).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, University of Jinan, Jinan, 250022, China

    Rongbin Xu, Huijie Shang, Dong Wang, Gaoqiang Yu & Yunguang Lin

  2. Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, 250022, China

    Dong Wang

Authors
  1. Rongbin Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huijie Shang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gaoqiang Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yunguang Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dong Wang .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Juan Carlos Figueroa-García

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Xu, R., Shang, H., Wang, D., Yu, G., Lin, Y. (2017). Prediction and Analysis of Mature microRNA with Flexible Neural Tree Model. In: Huang, DS., Jo, KH., Figueroa-García, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_74

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-63312-1_74

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63311-4

  • Online ISBN: 978-3-319-63312-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation