CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
Reseach Article

Article:Effect of Neural Network Parameters on RNA Secondary Structure Classification

by Ruchi Mann, Shailendra Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 23 - Number 6
Year of Publication: 2011
Authors: Ruchi Mann, Shailendra Singh
10.5120/2894-3786

Ruchi Mann, Shailendra Singh . Article:Effect of Neural Network Parameters on RNA Secondary Structure Classification. International Journal of Computer Applications. 23, 6 ( June 2011), 6-9. DOI=10.5120/2894-3786

@article{ 10.5120/2894-3786,
author = { Ruchi Mann, Shailendra Singh },
title = { Article:Effect of Neural Network Parameters on RNA Secondary Structure Classification },
journal = { International Journal of Computer Applications },
issue_date = { June 2011 },
volume = { 23 },
number = { 6 },
month = { June },
year = { 2011 },
issn = { 0975-8887 },
pages = { 6-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume23/number6/2894-3786/ },
doi = { 10.5120/2894-3786 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:09:25.969203+05:30
%A Ruchi Mann
%A Shailendra Singh
%T Article:Effect of Neural Network Parameters on RNA Secondary Structure Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 23
%N 6
%P 6-9
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Helix, Hairpin, Bulge, external loop, internal loop, multi-branch loop are the elements of RNA secondary structure. We have designed a neural network to classify the RNA sequence in to three categories i.e Hairpin, helix, neither of two. This can be extended to classify into all secondary structure elements. If all the elements are predicted then we can determine the entire structure of a RNA family. The parameters of neural network affect the performance of the network. But there are no rules to define the value of these parameters of network. For a given problem the optimal value of parameters can be obtained by performing the experiments on their values. This paper shows the effect on the performance of classification by varying the number of hidden layers, number of neurons and activation functions.

References
  1. Monther Aldwairi, Rehab Duwairi, Wafa’a Alqarqaz “A Classification System for Predicting RNA Hairpin Loops”, 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing
  2. Steeg E., Neural networks adaptive optimization and RNA secondary structure prediction, American Association for Artificial Intelligence, pp 121-160, 1993.
  3. The Vienna RNA Servers: RNAalifold server, at http://rna.tbi.univie.ac.at/cgibin/RNAalifold_beta.cgi.
  4. Siebert S, Backofen R, and MARNA: Multiple alignment and consensus structure prediction of RNAs based on sequence structure comparisons, BMC Bioinformatics, 21(16): 3352-3359, 2005.
  5. Knudsen B. Hein H., Pfold: RNA secondary structure prediction using stochastic context-free grammars, Nucleic Acids Research, 13(13): 3423-3428, 2003.
  6. Gardner P., Giegerich R., A Comprehensive Comparison Of Comparative RNA Structure Prediction approaches, BMC Bioinformatics, 5(140), 2004.
  7. D.H. Turner, Nearest neighbor parameters for RNA based on melting studies of synthetically constructed oligoribonucleotides, at http://www.bioinfo.rpi.edu/zukerm/rna/energy/node2.html.
  8. Christian Igel and Michael H¨usken “Improving the Rprop Learning Algorithm” , Proceedings of the Second International Symposium on Neural Computation, NC’2000, pp. 115–121, ICSC Academic Press, 2000
  9. M. Riedmiller and H. Braun. A direct adaptive method for faster backpropagation learning: The RPROP algorithm. In Proceedings of the IEEE International Conference on Neural Networks, pages 586–591. IEEE Press, 1993.
  10. R. A. Jacobs. Increased rates of convergence through learning rate adaptation. Neural Networks, 1(4):295–307, 1988
  11. Griffiths-Jones S, Bateman A, Marshall M, Khanna A and Eddy SR” Rfam: an RNA family database.” Nucleic acids research 2003;31;1;439-41
  12. S. Lindgreen, P. P. Gardner, and A. Krogh, "Measuring covariation in RNA alignments: physical realism improves information measures," Bioinformatics, vol. 22, pp. 2988-95, Dec 15 2006.
  13. D. H. Turner and N. Sugimoto, "RNA structure prediction," Annu Rev Biophys Biophys Chem, vol. 17, pp. 167-92, 1988.
  14. B. Knudsen and J. Hein, "Pfold: RNA secondary structure prediction using stochastic context-free grammars," Nucleic Acids Res, vol. 31, pp. 3423-8, Jul 1 2003.
  15. I. L. Hofacker, M. Fekete, and P. F. Stadler, "Secondary structure prediction for aligned RNA sequences," J Mol Biol, vol. 319, pp. 1059- 66, Jun 21 2002
  16. Nebel M., Identifying good predictions of RNA secondary structure, pacific symposium on biocomputing, 9: 423-434, 2004
  17. Bindewald E., Shapiro B., RNA secondary structure prediction from sequence alignments using a network of k-nearest neighbor classifiers, RNA, 12(3): 342 – 352, 2006.
  18. Tahi F., Gouy M , Regnier M , Automatic RNA secondary structure prediction with a comparative approach , Computers and Chemistry , 26:521-530, 2002.
  19. Gardner P., Giegerich R., A Comprehensive Comparison Of Comparative RNA Structure Prediction approaches, BMC Bioinformatics, 5(140), 2004.
  20. Hofacker I., Fekete M., Stadler PF., Secondary structure prediction for aligned RNA sequences, Journal of Molecular Biology, 319(5):1059-1066, 2002.
  21. Larranaga P., Calvo B., Santana R., Bielza C., Galdiano J., Inza I., Lozano J., Arman R., Santafe G., Perez A. , Robles V., Machine Learning In Bioinformatics, Oxford Journals, 7(1) : 86-112 , 2005.
  22. Ruan J., Stormo GD., Zhang W., An iterated loop matching approach to the prediction of RNA secondary structures with pseudoknots, 20(1):58-66, 2004.
  23. Rivas E, Eddy SR (2000) Secondary structure alone is generally not statistically significant for the detection of noncoding rnas. Bioinformatics16: 583–605.
  24. Werbos, P.J. (1993) “Backropagation through time: What it does and how to do it” Proc. ICNN,San Francisco, CA.
Index Terms

Computer Science
Information Sciences

Keywords

Classification RNA secondary structure neural networks activation function number of hidden layers