Discovery of structural and functional features in RNA pseudoknots

Chen, Qingfeng and Chen, Yi-Ping Phoebe 2009, Discovery of structural and functional features in RNA pseudoknots, IEEE transactions on knowledge and data engineering, vol. 21, no. 7, pp. 974-984.

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Title Discovery of structural and functional features in RNA pseudoknots
Author(s) Chen, Qingfeng
Chen, Yi-Ping Phoebe
Journal name IEEE transactions on knowledge and data engineering
Volume number 21
Issue number 7
Start page 974
End page 984
Total pages 11
Publisher IEEE
Place of publication Piscataway, NJ
Publication date 2009-07
ISSN 1041-4347
Keyword(s) RNA pseudoknots
Association rule mining
Summary An RNA pseudoknot consists of nonnested double-stranded stems connected by single-stranded loops. There is increasing recognition that RNA pseudoknots are one of the most prevalent RNA structures and fulfill a diverse set of biological roles within cells, and there is an expanding rate of studies into RNA pseudoknotted structures as well as increasing allocation of function. These not only produce valuable structural data but also facilitate an understanding of structural and functional characteristics in RNA molecules. PseudoBase is a database providing structural, functional, and sequence data related to RNA pseudoknots. To capture the features of RNA pseudoknots, we present a novel framework using quantitative association rule mining to analyze the pseudoknot data. The derived rules are classified into specified association groups regarding structure, function, and category of RNA pseudoknots. The discovered association rules assist biologists in filtering out significant knowledge of structure-function and structure-category relationships. A brief biological interpretation to the relationships is presented, and their potential correlations with each other are highlighted.
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890202 Application Tools and System Utilities
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
HERDC collection year 2009
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Document type: Journal Article
Collection: School of Information Technology
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