Classification of RNA sequences with pseudoknots using features based on partial sequences
Refereed conference paper presented and published in conference proceedings

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AbstractClassification 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(n3) 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.
All Author(s) ListTong K.-K., Cheung K.-Y., Lee K.-H., Leung K.-S.
Name of ConferenceIEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015
Start Date of Conference12/08/2015
End Date of Conference15/08/2015
Place of ConferenceNiagara Falls
Country/Region of ConferenceCanada
LanguagesEnglish-United Kingdom
Keywordsclassification, pseudoknot, RNA secondary structure prediction

Last updated on 2020-23-05 at 00:11