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Classification of RNA sequences with pseudoknots using features based on partial sequences
Date Issued
2015
Publisher
IEEE
ISBN
9781479969265
9781479969258
Citation
2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB
Type
Conference Paper
Abstract
Classification on pseudoknots existence is a challenging and meaningful problem in Bioinformatics. As predicting RNA secondary structures with pseudoknots is NP-complete problem while predicting pseudoknot-free structures can be done in O(n 3 ) time, if a preliminary pseudoknots existence classification of RNA sequence can be done before the prediction, the classification result can enhance the efficiency of RNA secondary structure prediction. In this paper, a classification of the existence of pseudoknots in an RNA sequence is presented. A set of features have been chosen by partial sequence content and thousands of RNA sequences with validated structures are used to train the classifier. Using a validated testing dataset, this classification method is shown to achieve a very good performance that the best result get 87% accuracy in 10-fold cross validation and around 75% accuracy in testing data. Moreover it may reveal how partial sequence content can affect the formation of pseudoknots.
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