Top > JACIII > Learning of Glycan Motifs Using Genetic Programming and Variou ...

single-jc.php

JACIII Vol.18 No.3 pp. 401-408
doi: 10.20965/jaciii.2014.p0401
(2014)

Paper:

Views over last 60 days: 739

Learning of Glycan Motifs Using Genetic Programming and Various Fitness Functions

Tetsuhiro Miyahara* and Tetsuji Kuboyama**

*Graduate School of Information Sciences, Hiroshima City University, 3-4-1 Ozuka-higashi, Asaminami-ku, Hiroshima 731-3194, Japan

**Computer Centre, Gakushuin University, 1-7-1 Mejiro, Toshima-ku, Tokyo 151-8588, Japan

Received:
October 15, 2013
Accepted:
March 2, 2014
Published:
May 20, 2014
Creative Commons License
Keywords:
genetic programming, tree patterns, glycan motifs
Abstract
We apply a genetic programming approach to learning of glycan motifs by using tag tree patterns and various fitness functions. Tag tree patterns obtained from some glycan data show characteristic tree structures. We examine the effects of using various fitness functions on GP processes and obtained glycan motifs. We also show that our method is applicable to tree structured data other than glycan data.
Cite this article as:
T. Miyahara and T. Kuboyama, “Learning of Glycan Motifs Using Genetic Programming and Various Fitness Functions,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.3, pp. 401-408, 2014.
Data files:
References
  1. [1] W. Banzhaf, P. Nordin, R. E. Keller, and F. D. Francone, “Genetic Programming: An Introduction : On the Automatic Evolution of Computer Programs and Its Applications,” Morgan Kaufmann, 1998.
  2. [2] J. R. Koza, “Genetic Programming: On the Programming of Computers by Means of Natural Selection,” MIT Press, 1992.
  3. [3] X. Yao (ed.), “Evolutionary Computation: Theory and Applications,” World Scientific, 1999.
  4. [4] K. C. Tan, M. H. Lim, X. Yao, and L. Wang (eds.), “Recent Advances in Simulated Evolution and Learning,” World Scientific, 2004.
  5. [5] K. Inata, T. Miyahara, H. Ueda, and K. Takahashi, “Evolution of characteristic tree structured patterns from semistructured documents,” Proc. AI-2006, Springer-Verlag, Vol.4304, pp. 1201-1207, 2006.
  6. [6] M. Nagamine, T. Miyahara, T. Kuboyama, H. Ueda, and K. Takahashi, “A genetic programming approach to extraction of glycan motifs using tree structured patterns,” Proc. AI-2007, Springer-Verlag, Vol.4830, pp. 150-159, 2007.
  7. [7] M. Nagamine, T. Miyahara, T. Kuboyama, H. Ueda, and K. Takahashi, “Evolution of multiple tree structured patterns from treestructured data using clustering,” Proc. AI-2008, Springer-Verlag, Vol.5360, pp. 500-511, 2008.
  8. [8] Y. Hizukuri, Y. Yamanishi, O. Nakamura, F. Yagi, S. Goto, and M. Kanehisa, “Extraction of leukemia specific glycan motifs in humans by computational glycomics,” Carbohydrate Research, Vol.340, pp. 2270-2278, 2005.
  9. [9] T. Kuboyama, K. Hirata, K.F. Aoki-Kinoshita, H. Kashima, and H. Yasuda, “A gram distribution kernel applied to glycan classification and motif extraction,” Genome Informatics, Vol.17, No.2, pp. 25-34, 2006.
  10. [10] T. Miyahara, Y. Suzuki, T. Shoudai, T. Uchida, K. Takahashi, and H. Ueda, “Discovery of frequent tag tree patterns in semistructured web documents,” Proc. PAKDD-2002, Springer-Verlag, LNAI 2336, pp. 341-355, 2002.
  11. [11] B.W. Matthews, “Comparison of the predicted and observed secondary structure of t4 phage lysozyme,” Biochim. Biophys. Acta, Vol.405, pp. 442-451, 1975.
  12. [12] R. Seehuus, A. Tveit, and O. Edsberg, “Discovering biological motifs with genetic programming,” Proc. Genetic and Evolutionary Computation Conference (GECCO) 2005, pp. 401-408, 2005.
  13. [13] T. Miyahara and T. Kuboyama, “Acquisition of glycan motifs using genetic programming and various fitness functions,” Proc. SCISISIS 2012, pp. 1684-1689, 2012.
  14. [14] Y. Suzuki, R. Akanuma, T. Shoudai, T. Miyahara, and T. Uchida, “Polynomial time inductive inference of ordered tree patterns with internal structured variables from positive data,” Proc. COLT-2002, Springer-Verlag, LNAI 2375, pp. 169-184, 2002.
  15. [15] K. Hashimoto, S. Goto, S. Kawano, K.F. Aoki-Kinoshita, N. Ueda, M. Hamajima, T. Kawasaki, and M. Kanehisa, “KEGG as a glycome informatics resource. Glycobiology,” Vol.16, pp. 63R-70R, 2006.
  16. [16] S. Doubet and P. Albersheim, “Clycobiology. Data Mining and Knowledge Discovery,” Vol.2, No.6, p. 505, 1992.
  17. [17] A. Moschitti, “Making tree kernels practical for natural language learning,” Proc. Conf. of the European Chapter of the Association for Computational Linguistics (EACL) 2006, 2006.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 22, 2024