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BioMed Research International
Volume 2015, Article ID 960108, 12 pages
http://dx.doi.org/10.1155/2015/960108
Research Article

Improved Pre-miRNA Classification by Reducing the Effect of Class Imbalance

1School of Computer Science and Technology, Key Laboratory of Database and Parallel Computing of Heilongjiang Province, Heilongjiang University, Harbin 150080, China
2School of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, China

Received 15 May 2015; Revised 18 October 2015; Accepted 20 October 2015

Academic Editor: Graziano Pesole

Copyright © 2015 Yingli Zhong 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. D. P. Bartel, “MicroRNAs: genomics, biogenesis, mechanism, and function,” Cell, vol. 116, no. 2, pp. 281–297, 2004. View at Publisher · View at Google Scholar · View at Scopus
  2. D. P. Bartel, “MicroRNAs: target recognition and regulatory functions,” Cell, vol. 136, no. 2, pp. 215–233, 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. S. L. Ameres and P. D. Zamore, “Diversifying microRNA sequence and function,” Nature Reviews Molecular Cell Biology, vol. 14, no. 8, pp. 475–488, 2013. View at Publisher · View at Google Scholar · View at Scopus
  4. B. Zhang, X. Pan, G. P. Cobb, and T. A. Anderson, “Plant microRNA: a small regulatory molecule with big impact,” Developmental Biology, vol. 289, no. 1, pp. 3–16, 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. M. Yousef, M. Nebozhyn, H. Shatkay, S. Kanterakis, L. C. Showe, and M. K. Showe, “Combining multi-species genomic data for microRNA identification using a Naïve Bayes classifier,” Bioinformatics, vol. 22, no. 11, pp. 1325–1334, 2006. View at Publisher · View at Google Scholar · View at Scopus
  6. M. Yousef, S. Jung, L. C. Showe, and M. K. Showe, “Learning from positive examples when the negative class is undetermined-microRNA gene identification,” Algorithms for Molecular Biology, vol. 3, article 2, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. K. L. S. Ng and S. K. Mishra, “De novo SVM classification of precursor microRNAs from genomic pseudo hairpins using global and intrinsic folding measures,” Bioinformatics, vol. 23, no. 11, pp. 1321–1330, 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. C. Xue, F. Li, T. He, G.-P. Liu, Y. Li, and X. Zhang, “Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine,” BMC Bioinformatics, vol. 6, article 310, 2005. View at Publisher · View at Google Scholar · View at Scopus
  9. J. Ding, S. Zhou, and J. Guan, “MiRenSVM: towards better prediction of microRNA precursors using an ensemble SVM classifier with multi-loop features,” BMC Bioinformatics, vol. 11, supplement 11, article S11, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. P. Jiang, H. Wu, W. Wang, W. Ma, X. Sun, and Z. Lu, “MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features,” Nucleic Acids Research, vol. 35, no. 2, pp. W339–W344, 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. J.-W. Nam, K.-R. Shin, J. Han, Y. Lee, V. N. Kim, and B.-T. Zhang, “Human microRNA prediction through a probabilistic co-learning model of sequence and structure,” Nucleic Acids Research, vol. 33, no. 11, pp. 3570–3581, 2005. View at Publisher · View at Google Scholar · View at Scopus
  12. P. Xuan, M. Guo, Y. Huang, W. Li, and Y. Huang, “Maturepred: efficient identification of microRNAs within novel plant pre-miRNAs,” PLoS ONE, vol. 6, no. 11, Article ID e27422, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. M. Leclercq, A. B. Diallo, and M. Blanchette, “Computational prediction of the localization of microRNAs within their pre-miRNA,” Nucleic Acids Research, vol. 41, no. 15, pp. 7200–7211, 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. C. He, Y.-X. Li, G. Zhang et al., “MiRmat: mature microRNA sequence prediction,” PLoS ONE, vol. 7, no. 12, Article ID e51673, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. G. M. Weiss, “Mining with rarity: a unifying framework,” ACM SIGKDD Explorations Newsletter, vol. 6, no. 1, pp. 7–19, 2004. View at Publisher · View at Google Scholar
  16. P. Xuan, M. Guo, X. Liu, Y. Huang, W. Li, and Y. Huang, “PlantMiRNAPred: efficient classification of real and pseudo plant pre-miRNAs,” Bioinformatics, vol. 27, no. 10, Article ID btr153, pp. 1368–1376, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. A. Gudyś, M. W. Szcześniak, M. Sikora, and I. Makałowska, “HuntMi: an efficient and taxon-specific approach in pre-miRNA identification,” BMC Bioinformatics, vol. 14, article 83, 2013. View at Publisher · View at Google Scholar · View at Scopus
  18. L. Wei, M. Liao, Y. Gao, R. Ji, Z. He, and Q. Zou, “Improved and promising identification of human microRNAs by incorporating a high-quality negative set,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 11, no. 1, pp. 192–201, 2014. View at Publisher · View at Google Scholar · View at Scopus
  19. R. Batuwita and V. Palade, “MicroPred: effective classification of pre-miRNAs for human miRNA gene prediction,” Bioinformatics, vol. 25, no. 8, pp. 989–995, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: synthetic minority over-sampling technique,” Journal of Artificial Intelligence Research, vol. 16, no. 1, pp. 321–357, 2002. View at Google Scholar · View at Scopus
  21. Y. Sun, M. S. Kamel, A. K. C. Wong, and Y. Wang, “Cost-sensitive boosting for classification of imbalanced data,” Pattern Recognition, vol. 40, no. 12, pp. 3358–3378, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  22. Y. Freund and R. E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” Journal of Computer and System Sciences, vol. 55, no. 1, pp. 119–139, 1997. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  23. P. Sætrom, O. Snøve Jr., M. Nedland et al., “Conserved microRNA characteristics in mammals,” Oligonucleotides, vol. 16, no. 2, pp. 115–144, 2006. View at Publisher · View at Google Scholar · View at Scopus
  24. B. Zhang, X. Pan, C. H. Cannon, G. P. Cobb, and T. A. Anderson, “Conservation and divergence of plant microRNA genes,” The Plant Journal, vol. 46, no. 2, pp. 243–259, 2006. View at Publisher · View at Google Scholar · View at Scopus
  25. I. L. Hofacker, B. Priwitzer, and P. F. Stadler, “Prediction of locally stable RNA secondary structures for genome-wide surveys,” Bioinformatics, vol. 20, no. 2, pp. 186–190, 2004. View at Publisher · View at Google Scholar · View at Scopus
  26. D. J. Rogers and T. T. Tanimoto, “A computer program for classifying plants,” Science, vol. 132, no. 3434, pp. 1115–1118, 1960. View at Publisher · View at Google Scholar · View at Scopus
  27. A. Kozomara and S. J. Griffiths, “MiRBase: annotating high confidence microRNAs using deep sequencing data,” Nucleic Acids Research, vol. 42, no. 1, pp. D68–D73, 2014. View at Publisher · View at Google Scholar · View at Scopus