MiRNA Recognition with the yasMiR System: The Quest for Further Improvements
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.
KeywordsFolding Minimum Free Energy Triplet Pattern Simple Statistical Measure Mutual Information Statistic Random Forest Machine
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  offers a unified view on the past  and present work on yasMiR.
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