MiRNA Recognition with the yasMiR System: The Quest for Further Improvements

  • Daniel Pasailă
  • Andrei Sucilă
  • Irina Mohorianu
  • Ştefan Panţiru
  • Liviu Ciortuz
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 696)


The paper “Using Base Pairing Probabilities for MiRNA Recognition” by Daniel Pasailă, Irina Mohorianu, and Liviu Ciortuz, that has been published in Proceedings of the International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) 2008, IEEE Computer Society, pp. 519–525, has introduced a new SVM for microRNA identification, whose novelty is twofolded: first, many of its features incorporate the base-pairing probabilities provided by McCaskill’s algorithm, and second the classification performance is improved using a certain similarity (“profile”-based) measure between the training and test microRNAs and a set of carefully chosen (“pivot”) RNA sequences. Comparisons with some of the best existing SVMs for microRNA identification proved that our SVM obtains truly competitive results. Here we add several significant extensions to the work reported in Daniel Pasailă et al. Proceedings of the International (SYNASC) 2008, pp. 519–525: testing this classifier on a more recent version of miRBase (12.0), evaluating the effect of using probabilistic patterns instead of non-probabilistic ones, analysing the discriminative power of different categories of features we used, and automatically searching for good pivot RNA sequences, which are critical for classification in our approach.


Folding Minimum Free Energy Triplet Pattern Simple Statistical Measure Mutual Information Statistic Random Forest Machine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



LC thanks Dr. Mihaela Zavolan from Biozentrum, University of Basel, Dr. Hélène Touzet from the University of Lille, and Dr. Marti Tammi from the National University of Singapore for useful discussions on miRNA identification.

The source code of our system and the datasets we used can be found at the address www.info.uaic.ro/~ciortuz/yasmir. The technical report [4] offers a unified view on the past [3] and present work on yasMiR.


  1. 1.
    Andrew Fire, Siqun Xu, Mary Montgomery, Steven Kostas, Samuel Driver, and Craig Mello. Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature, 391(6669):806–811, 1998PubMedCrossRefGoogle Scholar
  2. 2.
    Chenghai Xue, Fei Li, Tao He, Guoping Liu, Yanda Li, and Xuegong Zhang. Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. BMC Bioinformatics, 6(310), 2005CrossRefGoogle Scholar
  3. 3.
    Daniel Pasailă, Irina Mohorianu, and Liviu Ciortuz. Using base pairing probabilities for MiRNA recognition. In SYNASC ’08: Proceedings of the 2008 10th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, pages 519–525, 2008Google Scholar
  4. 4.
    Daniel Pasailă, Irina Mohorianu, Andrei Sucilă, Ştefan Panţiru, and Liviu Ciortuz. Yet another SVM for miRNA recognition: yasMiR, 2010. Technical Report TR-10-01, Faculty of Computer Science, University of Iasi, RomaniaGoogle Scholar
  5. 5.
    Jacek Biesiada and Wlodzislaw Duch. Feature selection for high-dimensional data: A Kolmogorov–Smirnov correlation-based filter. Computer Recognition Systems, 30:95–103, 2005CrossRefGoogle Scholar
  6. 6.
    John S. McCaskill. The equilibrium partition function and base pair binding probabilities for RNA secondary structures. Biopolymers, 29:1105–1119, 1990PubMedCrossRefGoogle Scholar
  7. 7.
    Kwang Loong Stanley Ng and Santosh Mishra. De novo SVM classification of precursor microRNAs from genomic pseudo hairpins using global and intrinsic folding measures. Bioinformatics, 23(11):1321–1330, 2007PubMedCrossRefGoogle Scholar
  8. 8.
    Nello Cristianini and John Shawe-Taylor. An introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, New York, NY, USA, 2000Google Scholar
  9. 9.
    Wenjie Shu, Xiaochen Bo, Zhiqiang Zheng, and Shengqi Wang. A novel representation of RNA secondary structure based on element-contact graphs. BMC Bioinformatics, 9(1):188, 2008CrossRefGoogle Scholar
  10. 10.
    Yunpen Xu, Xuefeng Zhou, and Weixiong Zhang. MicroRNA prediction with a novel ranking algorithm based on random walks. Bioinformatics, 24(13), 2008Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Daniel Pasailă
  • Andrei Sucilă
  • Irina Mohorianu
  • Ştefan Panţiru
  • Liviu Ciortuz
    • 1
  1. 1.Department of Computer Science“Alexandru Ioan Cuza” University of IaşiIaşiRomania

Personalised recommendations