Classification of RNAs with Pseudoknots using k-mer Occurrences Count as Attributes
Refereed conference paper presented and published in conference proceedings

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AbstractRNAs are functionally important in many biological processes. Predicting secondary structures of RNAs can help understanding 3D structures and functions of RNAs. However, RNA secondary structure prediction with pseudoknots is NP-complete. Predicting whether the RNAs contain pseudoknots in advance can save computation time as secondary structure prediction without pseudoknots is much faster. In this paper, we use k-mer occurrences as attributes to predict whether the RNAs have pseudoknots in the secondary structure. The results show two classifiers can predict 90% of the instance correctly.
All Author(s) ListCheung KY, Tong KK, Lee KH, Leung KS
Name of ConferenceIEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE)
Start Date of Conference10/11/2013
End Date of Conference13/11/2013
Place of ConferenceChania
Country/Region of ConferenceGreece
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesEngineering; Engineering, Biomedical; Medical Informatics

Last updated on 2020-27-10 at 01:04