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BioMed Research International
Volume 2017, Article ID 7323508, 8 pages
https://doi.org/10.1155/2017/7323508
Research Article

Comparative Analysis and Classification of Cassette Exons and Constitutive Exons

1School of Mechano-Electronic Engineering, Xidian University, Xi’an 710071, China
2Center for Polymer Studies and Department of Physics, Boston University, Boston, MA 02215, USA
3School of Economics and Management, Xidian University, Xi’an 710071, China

Correspondence should be addressed to Meng Cai; ude.ub@iacm

Received 18 August 2017; Accepted 13 November 2017; Published 4 December 2017

Academic Editor: Yudong Cai

Copyright © 2017 Ying Cui et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. E. Petrillo, M. A. Godoy Herz, A. Barta, M. Kalyna, and A. R. Kornblihtt, “Let there be light: regulation of gene expression in plants,” RNA Biology, vol. 11, no. 10, pp. 1215–1220, 2014. View at Publisher · View at Google Scholar · View at Scopus
  2. K. D. Raczynska, A. Stepien, D. Kierzkowski et al., “The SERRATE protein is involved in alternative splicing in Arabidopsis thaliana,” Nucleic Acids Research, vol. 42, no. 2, pp. 1224–1244, 2014. View at Publisher · View at Google Scholar · View at Scopus
  3. Q. Pan, O. Shai, L. J. Lee, B. J. Frey, and B. J. Blencowe, “Erratum: Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing (Nature Genetics (2008) 40 (1413-1415)),” Nature Genetics, vol. 41, no. 6, p. 762, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. Y. Marquez, J. W. S. Brown, C. G. Simpson, A. Barta, and M. Kalyna, “Transcriptome survey reveals increased complexity of the alternative splicing landscape in Arabidopsis,” Genome Research, vol. 22, no. 6, pp. 1184–1195, 2012. View at Publisher · View at Google Scholar · View at Scopus
  5. H. Dvinge and R. K. Bradley, “Widespread intron retention diversifies most cancer transcriptomes,” Genome Medicine, vol. 7, no. 1, article no. 45, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. S. Oltean and D. O. Bates, “Hallmarks of alternative splicing in cancer,” Oncogene, vol. 33, no. 46, pp. 5311–5318, 2014. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Danan-Gotthold, R. Golan-Gerstl, E. Eisenberg, K. Meir, R. Karni, and E. Y. Levanon, “Identification of recurrent regulated alternative splicing events across human solid tumors,” Nucleic Acids Research, vol. 43, no. 10, pp. 5130–5144, 2015. View at Publisher · View at Google Scholar · View at Scopus
  8. S. C.-W. Lee and O. Abdel-Wahab, “Therapeutic targeting of splicing in cancer,” Nature Medicine, vol. 22, no. 9, pp. 976–986, 2016. View at Publisher · View at Google Scholar · View at Scopus
  9. Y. Christinat, R. Pawłowski, and W. Krek, “JSplice: A high-performance method for accurate prediction of alternative splicing events and its application to large-scale renal cancer transcriptome data,” Bioinformatics, vol. 32, no. 14, pp. 2111–2119, 2016. View at Publisher · View at Google Scholar · View at Scopus
  10. E. M. McNally and E. J. Wyatt, “Welcome to the splice age: Antisense oligonucleotide-mediated exon skipping gains wider applicability,” The Journal of Clinical Investigation, vol. 126, no. 4, pp. 1236–1238, 2016. View at Publisher · View at Google Scholar · View at Scopus
  11. Y. Kim, H. R. Kim, J. Kim et al., “A novel synonymous mutation causing complete skipping of exon 16 in the SLC26A4 gene in a Korean family with hearing loss,” Biochemical and Biophysical Research Communications, vol. 430, no. 3, pp. 1147–1150, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. S. Tei, H. T. Ishii, H. Mitsuhashi, and S. Ishiura, “Antisense oligonucleotide-mediated exon skipping of CHRNA1 pre-mRNA as potential therapy for Congenital Myasthenic Syndromes,” Biochemical and Biophysical Research Communications, vol. 461, no. 3, pp. 481–486, 2015. View at Publisher · View at Google Scholar · View at Scopus
  13. G. Dror, R. Sorek, and R. Shamir, “Accurate identification of alternatively spliced exons using support vector machine,” Bioinformatics, vol. 21, no. 7, pp. 897–901, 2005. View at Publisher · View at Google Scholar · View at Scopus
  14. G. Su, Y.-F. Sun, and J. Li, “The identification of human cryptic exons based on SVM,” in Proceedings of the 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009, China, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. L. Li, Q. Peng, A. Shakoor, T. Zhong, S. Sun, and X. Wang, “A classification of alternatively spliced cassette exons using AdaBoost-based algorithm,” in Proceedings of the 2014 IEEE International Conference on Information and Automation, ICIA 2014, pp. 370–375, China, July 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. X. Zhang, Q. Peng, L. Li, and X. Li, “Recognition of alternatively spliced cassette exons based on a hybrid model,” Biochemical and Biophysical Research Communications, vol. 471, no. 3, pp. 368–372, 2016. View at Publisher · View at Google Scholar · View at Scopus
  17. A. Busch and K. J. Hertel, “HEXEvent: A database of human EXon splicing Events,” Nucleic Acids Research, vol. 41, no. 1, pp. D118–D124, 2013. View at Publisher · View at Google Scholar · View at Scopus
  18. L. R. Meyer, A. S. Zweig, A. S. Hinrichs et al., “The UCSC Genome Browser Database: update 2006,” Nucleic Acids Research, vol. 34, no. 90001, pp. D590–D598, 2006. View at Publisher · View at Google Scholar
  19. G. Yeo and C. B. Burge, “Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals,” Journal of Computational Biology, vol. 11, no. 2-3, pp. 377–394, 2004. View at Publisher · View at Google Scholar · View at Scopus
  20. L. E. Peterson, “K-nearest neighbor,” Scholarpedia, vol. 4, no. 2, article 1883, 2009. View at Publisher · View at Google Scholar
  21. V. N. Vapnik, The Nature of Statistical Learning Theory, Springer, 2000. View at Publisher · View at Google Scholar · View at MathSciNet
  22. Y. Cui, J. Han, D. Zhong, and R. Liu, “A novel computational method for the identification of plant alternative splice sites,” Biochemical and Biophysical Research Communications, vol. 431, no. 2, pp. 221–224, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. J. Zhang, J. Xu, X. Hu et al., “Diagnostic method of diabetes based on support vector machine and tongue images,” BioMed Research International, vol. 2017, Article ID 7961494, 9 pages, 2017. View at Publisher · View at Google Scholar · View at Scopus
  24. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. View at Publisher · View at Google Scholar · View at Scopus
  25. H. Kazan, “Modeling Gene Regulation in Liver Hepatocellular Carcinoma with Random Forests,” BioMed Research International, vol. 2016, Article ID 1035945, 2016. View at Publisher · View at Google Scholar · View at Scopus
  26. X.-Y. Pan, Y.-N. Zhang, and H.-B. Shen, “Large-scale prediction of human protein-protein interactions from amino acid sequence based on latent topic features,” Journal of Proteome Research, vol. 9, no. 10, pp. 4992–5001, 2010. View at Publisher · View at Google Scholar · View at Scopus
  27. X. Pan, L. Zhu, Y.-X. Fan, and J. Yan, “Predicting protein-RNA interaction amino acids using random forest based on submodularity subset selection,” Computational Biology and Chemistry, vol. 53, pp. 324–330, 2014. View at Publisher · View at Google Scholar · View at Scopus
  28. T. Hothorn, K. Hornik, and A. Zeileis, “Unbiased recursive partitioning: a conditional inference framework,” Journal of Computational and Graphical Statistics, vol. 15, no. 3, pp. 651–674, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  29. C. Strobl, A.-L. Boulesteix, T. Kneib, T. Augustin, and A. Zeileis, “Conditional variable importance for random forests,” BMC Bioinformatics, vol. 9, no. 1, article 307, 2008. View at Publisher · View at Google Scholar · View at Scopus
  30. T. Chen and C. Guestrin, “XGBoost: a scalable tree boosting system,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 785–794, August 2016. View at Publisher · View at Google Scholar · View at Scopus
  31. J. H. Friedman, “Greedy function approximation: a gradient boosting machine,” The Annals of Statistics, vol. 29, no. 5, pp. 1189–1232, 2001. View at Publisher · View at Google Scholar · View at MathSciNet