We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
Reseach Article

On Support Vector Machine Ensembles for Classification of Recombination Breakpoint Regions in Saccharomyces Cerevisiae

by Ashok Kumar Dwivedi, Usha Chouhan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 108 - Number 13
Year of Publication: 2014
Authors: Ashok Kumar Dwivedi, Usha Chouhan
10.5120/18975-0475

Ashok Kumar Dwivedi, Usha Chouhan . On Support Vector Machine Ensembles for Classification of Recombination Breakpoint Regions in Saccharomyces Cerevisiae. International Journal of Computer Applications. 108, 13 ( December 2014), 44-48. DOI=10.5120/18975-0475

@article{ 10.5120/18975-0475,
author = { Ashok Kumar Dwivedi, Usha Chouhan },
title = { On Support Vector Machine Ensembles for Classification of Recombination Breakpoint Regions in Saccharomyces Cerevisiae },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 108 },
number = { 13 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 44-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume108/number13/18975-0475/ },
doi = { 10.5120/18975-0475 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:42:55.959749+05:30
%A Ashok Kumar Dwivedi
%A Usha Chouhan
%T On Support Vector Machine Ensembles for Classification of Recombination Breakpoint Regions in Saccharomyces Cerevisiae
%J International Journal of Computer Applications
%@ 0975-8887
%V 108
%N 13
%P 44-48
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recombination has major influence on evolution. Recombination occurs at specific region on chromosomes more frequently than other regions. Chromosomal region where recombination occurs more frequently is hot recombination region, whereas, the region where recombination occurs less frequently is cold recombination region. In this paper, supervised machine learning model based on support vector machine and ensembles of support vector machine have been devised for the efficient and effective classification of hot and cold recombination regions based on the compositional features of nucleotide sequences. Models were validated using tenfold cross validation techniques. These models gave high classification accuracy of 87. 0%, 91. 58%, and 92. 14 % using support vector machine and its boosting and bagging ensembles respectively. Moreover, support vector machine ensemble with bagging gave remarkably high area under receiver operating curve of . 9580. Furthermore, results indicate that bagging ensembles achieved the best result while used for the performance improvement of support vector machines.

References
  1. L. Hansen, N. -K. Kim, L. Mariño-Ramírez, and D. Landsman, "Analysis of biological features associated with meiotic recombination hot and cold spots in Saccharomyces cerevisiae," PloS one, vol. 6, p. e29711, 2011.
  2. G. R. Smith, "Homologous recombination near and far from DNA breaks: alternative roles and contrasting views," Annual review of genetics, vol. 35, pp. 243-274, 2001.
  3. L. Kauppi, A. J. Jeffreys, and S. Keeney, "Where the crossovers are: recombination distributions in mammals," Nature Reviews Genetics, vol. 5, pp. 413-424, 2004.
  4. S. Myers, L. Bottolo, C. Freeman, G. McVean, and P. Donnelly, "A fine-scale map of recombination rates and hotspots across the human genome," Science, vol. 310, pp. 321-324, 2005.
  5. F. Baudat and A. Nicolas, "Clustering of meiotic double-strand breaks on yeast chromosome III," Proceedings of the National Academy of Sciences, vol. 94, pp. 5213-5218, 1997.
  6. S. Klein, D. Zenvirth, V. Dror, A. B. Barton, D. B. Kaback, and G. Simchen, "Patterns of meiotic double-strand breakage on native and artificial yeast chromosomes," Chromosoma, vol. 105, pp. 276-284, 1996.
  7. D. Zenvirth, T. Arbel, A. Sherman, M. Goldway, S. Klein, and G. Simchen, "Multiple sites for double-strand breaks in whole meiotic chromosomes of Saccharomyces cerevisiae," The EMBO journal, vol. 11, p. 3441, 1992.
  8. T. D. Petes, "Meiotic recombination hot spots and cold spots," Nature Reviews Genetics, vol. 2, pp. 360-369, 2001.
  9. K. P. Kohl and J. Sekelsky, "Meiotic and mitotic recombination in meiosis," Genetics, vol. 194, pp. 327-334, 2013.
  10. M. Lichten and A. S. Goldman, "Meiotic recombination hotspots," Annual review of genetics, vol. 29, pp. 423-444, 1995.
  11. A. J. Jeffreys, J. K. Holloway, L. Kauppi, C. A. May, R. Neumann, M. T. Slingsby, et al. , "Meiotic recombination hot spots and human DNA diversity," Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, vol. 359, pp. 141-152, 2004.
  12. W. P. Wahls, "2 Meiotic Recombination Hotspots: Shaping the Genome and Insights into Hypervariable Minisatellite DNA Change," Current topics in developmental biology, vol. 37, pp. 37-75, 1997.
  13. J. L. Gerton, J. DeRisi, R. Shroff, M. Lichten, P. O. Brown, and T. D. Petes, "Global mapping of meiotic recombination hotspots and coldspots in the yeast Saccharomyces cerevisiae," Proceedings of the National Academy of Sciences, vol. 97, pp. 11383-11390, 2000.
  14. R. M. Kliman, N. Irving, and M. Santiago, "Selection conflicts, gene expression, and codon usage trends in yeast," Journal of molecular evolution, vol. 57, pp. 98-109, 2003.
  15. R. M. Kliman and J. Hey, "Reduced natural selection associated with low recombination in Drosophila melanogaster," Molecular Biology and Evolution, vol. 10, pp. 1239-1258, 1993.
  16. G. Marais, D. Mouchiroud, and L. Duret, "Does recombination improve selection on codon usage? Lessons from nematode and fly complete genomes," Proceedings of the National Academy of Sciences, vol. 98, pp. 5688-5692, 2001.
  17. G. Marais and G. Piganeau, "Hill-Robertson interference is a minor determinant of variations in codon bias across Drosophila melanogaster and Caenorhabditis elegans genomes," Molecular biology and evolution, vol. 19, pp. 1399-1406, 2002.
  18. J. Perry and A. Ashworth, "Evolutionary rate of a gene affected by chromosomal position," Current biology, vol. 9, pp. 987-S3, 1999.
  19. S. M. Fullerton, A. B. Carvalho, and A. G. Clark, "Local rates of recombination are positively correlated with GC content in the human genome," Molecular biology and evolution, vol. 18, pp. 1139-1142, 2001.
  20. C. C. Friedel, K. H. Jahn, S. Sommer, S. Rudd, H. W. Mewes, and I. V. Tetko, "Support vector machines for separation of mixed plant–pathogen EST collections based on codon usage," Bioinformatics, vol. 21, pp. 1383-1388, 2005.
  21. K. Lin, Y. Kuang, J. S. Joseph, and P. R. Kolatkar, "Conserved codon composition of ribosomal protein coding genes in Escherichia coli, Mycobacterium tuberculosis and Saccharomyces cerevisiae: lessons from supervised machine learning in functional genomics," Nucleic acids research, vol. 30, pp. 2599-2607, 2002.
  22. G. Liu, J. Liu, X. Cui, and L. Cai, "Sequence-dependent prediction of recombination hotspots in< i> Saccharomyces cerevisiae," Journal of theoretical biology, vol. 293, pp. 49-54, 2012.
  23. W. -R. Qiu, X. Xiao, and K. -C. Chou, "iRSpot-TNCPseAAC: Identify recombination spots with trinucleotide composition and pseudo amino acid components," International journal of molecular sciences, vol. 15, pp. 1746-1766, 2014.
  24. X. Xia and Z. Xie, "DAMBE: software package for data analysis in molecular biology and evolution," Journal of Heredity, vol. 92, pp. 371-373, 2001.
  25. T. Carver and A. Bleasby, "The design of Jemboss: a graphical user interface to EMBOSS," Bioinformatics, vol. 19, pp. 1837-1843, 2003.
  26. V. N. Vapnik and V. Vapnik, Statistical learning theory vol. 2: Wiley New York, 1998.
  27. V. Vapnik, The nature of statistical learning theory: springer, 2000.
  28. C. J. Burges, "A tutorial on support vector machines for pattern recognition," Data mining and knowledge discovery, vol. 2, pp. 121-167, 1998.
Index Terms

Computer Science
Information Sciences

Keywords

Recombination Support Vector Machine Boosting Bagging Classification