Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7504
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dc.contributor.authorCheung, Kwan-Yauen_US
dc.contributor.authorTong, Kwok-Kiten_US
dc.contributor.authorLee, Kin-Hongen_US
dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.date.accessioned2023-03-16T04:24:08Z-
dc.date.available2023-03-16T04:24:08Z-
dc.date.issued2013-
dc.identifier.citation13th IEEE International Conference on BioInformatics and BioEngineering, 2013, pp. 1-4,en_US
dc.identifier.isbn978-1-4799-3163-7-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7504-
dc.description.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.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof13th IEEE International Conference on BioInformatics and BioEngineeringen_US
dc.titleClassification of RNAs with pseudoknots using k-mer occurrences count as attributesen_US
dc.typeConference Paperen_US
dc.identifier.doi10.1109/BIBE.2013.6701575.-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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