Skip to main content
SpringerLink
Log in

Detecting RNA Sequences Using Two-Stage SVM Classifier

  • Conference paper
Life System Modeling and Simulation (LSMS 2007)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 4689))

Included in the following conference series:

  • 1614 Accesses

Abstract

RNA sequences detection is time-consuming because of its huge data set size. Although SVM has been proved to be useful, normal SVM is not suitable for classification of large data sets because of its high training complexity. A two-stage SVM classification approach is introduced for fast classifying large data sets. Experimental results on several RNA sequences detection demonstrate that the proposed approach is promising for such applications.

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

Access this chapter

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Awad, M.L., Khan, F., Bastani, I., Yen, L.: An Effective support vector machine(SVMs) Performance Using Hierarchical Clustering. In: Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence, pp. 663–667. IEEE Computer Society Press, Los Alamitos (2004)

    Chapter  Google Scholar 

  2. Axmann, I.M., Kensche, P., Vogel, J., Kohl, S., Herzel, H., Hess, W.R.: Identification of cyanobacterial non-coding RNAs by comparative genome analysis. Genome Biol. R73 6 (2005)

    Google Scholar 

  3. Babu, G., Murty, M.: A near-optimal initial seed value selection in K-means algorithm using a genetic algorithm. Pattern Recognit. Lett. 14, 763–769 (1993)

    Article  MATH  Google Scholar 

  4. Cervantes, J., Li, X., Yu, W.: Support Vector Machine Classification Based on Fuzzy Clustering for Large Data Sets. In: Gelbukh, A., Reyes-Garcia, C.A. (eds.) MICAI 2006. LNCS (LNAI), vol. 4293, pp. 572–582. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  5. Cervantes, J., Li, X., Yu, W., Li, K.: Support vector machine classification for large data sets via minimum enclosing ball clustering. Neurocomputing (accepted for publication)

    Google Scholar 

  6. Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm

  7. Chen, P.H., Fan, R.E., Lin, C.J.: A Study on SMO-Type Decomposition Methods for Support Vector Machines. IEEE Trans. Neural Networks 17, 893–908 (2006)

    Article  Google Scholar 

  8. Collobert, R., Bengio, S.: SVMTorch: Support vector machines for large regression problems. Journal of Machine Learning Research 1, 143–160 (2001)

    Article  MathSciNet  Google Scholar 

  9. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge (2000)

    Google Scholar 

  10. Dong, J.X., Krzyzak, A., Suen, C.Y.: Fast SVM Training Algorithm with Decomposition on Very Large Data Sets. IEEE Trans. Pattern Analysis and Machine Intelligence 27, 603–618 (2005)

    Article  Google Scholar 

  11. Folino, G., Pizzuti, C., Spezzano, G.: GP Ensembles for Large-Scale Data Classification. IEEE Trans. Evol. Comput. 10, 604–616 (2006)

    Article  Google Scholar 

  12. Griffiths-Jones, S., Moxon, S., Marshall, M., Khanna, A., Eddy, S.R., Bateman, A.: RFAM: annotating non-coding RNAs in complete genomes. Nucleic Acids. Res. 33, 121–124 (2005)

    Article  Google Scholar 

  13. Girolami, M.: Mercer kernel based clustering in feature space. IEEE Trans. Neural Networks 13, 780–784 (2002)

    Article  Google Scholar 

  14. Hansen, J.L., Schmeing, T.M., Moore, P.B., Steitz, T.A.: Structural insights into peptide bond formation. Proc. Natl. Acad. Sci. 99, 11670–11675 (2002)

    Article  Google Scholar 

  15. Huang, G.B., Mao, K.Z., Siew, C.K., Huang, D.S.: Fast Modular Network Implementation for Support Vector Machines. IEEE Trans. on Neural Networks (2006)

    Google Scholar 

  16. Joachims, T.: Making large-scale support vector machine learning practice. Advances in Kernel Methods: Support Vector Machine. MIT Press, Cambridge (1998)

    Google Scholar 

  17. Kim, S.W., Oommen, B.J.: Enhancing Prototype Reduction Schemes with Recursion: A Method Applicable for Large Data Sets. IEEE Trans. Syst. Man, Cybern. B. 34, 1184–1397 (2004)

    Article  Google Scholar 

  18. Li, X., Cervantes, J., Yu, W.: Two Stages SVM Classification for Large Data Sets via Randomly Reducing and Recovering Training Data. In: IEEE International Conference on Systems, Man, and Cybernetics, Montreal Canada (2007)

    Google Scholar 

  19. Lin, C.T., Yeh, L.C.M., S, F., Chung, J.F., Kumar, N.: Support-Vector-Based Fuzzy Neural Network for Pattern Classification. IEEE Trans. Fuzzy Syst. 14, 31–41 (2006)

    Article  Google Scholar 

  20. Mavroforakis, M.E., Theodoridis, S.: A Geometric Approach to Support Vector Machine(SVM) Classification. IEEE Trans. Neural Networks 17, 671–682 (2006)

    Article  Google Scholar 

  21. Noble, W.S., Kuehn, S., Thurman, R., Yu, M., Stamatoyannopoulos, J.: Predicting the in vivo signature of human gene regulatory sequences. Bioinformatics 21, 338–343 (2005)

    Article  Google Scholar 

  22. Pal, N., Bezdek, J.: On cluster validity for the fuzzy c-means model. IEEE Trans. Fuzzy Syst. 3, 370–379 (1995)

    Article  Google Scholar 

  23. Platt, J.: Fast Training of support vector machine using sequential minimal optimization. Advances in Kernel Methods: support vector machine. MIT Press, Cambridge, MA (1998)

    Google Scholar 

  24. Prokhorov, D.: IJCNN 2001 neural network competition. Ford Research Laboratory (2001), http://www.geocities.com/ijcnn/nnc_ijcnn01.pdf

  25. Shih, L., Rennie, D.M., Chang, Y., Karger, D.R.: Text Bundling: Statistics-based Data Reduction. In: Proc. of the Twentieth Int. Conf. on Machine Learning, Washington DC (2003)

    Google Scholar 

  26. Tresp, V.: A Bayesian Committee Machine. Neural Computation 12, 2719–2741 (2000)

    Article  Google Scholar 

  27. Uzilov, A.V., Keegan, J.M., Mathews, D.H.: Detection of non-coding RNAs on the basis of predicted secondary structure formation free energy change. BMC Bioinformatics 173 (2006)

    Google Scholar 

  28. Washietl, S., Hofacker, I.L., Lukasser, M., Huttenhofer, A., Stadler, P.F.: Mapping of conserved RNA secondary structures predicts thousands of functional noncoding RNAs in the human genome. Nat. Biotechnol. 23, 1383–1390 (2005)

    Article  Google Scholar 

  29. Weilbacher, T., Suzuki, K., Dubey, A.K., Wang, X., Gudapaty, S., Morozov, I., Baker, C.S., Georgellis, D., Babitzke, P., Romeo, T.: A novel sRNA component of the carbon storage regulatory system of Escherichia coli. Mol. Microbiol. 48, 657–670 (2003)

    Article  Google Scholar 

  30. Xu, R., WunschII, D.: Survey of Clustering Algorithms. IEEE Trans. Neural Networks 16, 645–678 (2005)

    Article  Google Scholar 

  31. Yu, H., Yang, J., Han, J.: Classifying Large Data Sets Using SVMs with Hierarchical Clusters. In: Proc. of the 9th ACM SIGKDD, ACM Press, New York (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Departamento de Computación, CINVESTAV-IPN, A.P. 14-740, Av.IPN 2508, México D.F., 07360, México

    Xiaoou Li

  2. School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast Ashby Building, Stranmillis Road, Belfast, BT9 5AH, UK

    Kang Li

Authors
  1. Xiaoou Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Kang Li Xin Li George William Irwin Gusen He

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Li, X., Li, K. (2007). Detecting RNA Sequences Using Two-Stage SVM Classifier. In: Li, K., Li, X., Irwin, G.W., He, G. (eds) Life System Modeling and Simulation. LSMS 2007. Lecture Notes in Computer Science(), vol 4689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74771-0_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-74771-0_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74770-3

  • Online ISBN: 978-3-540-74771-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation