Issue No. 07 - July (2009 vol. 21)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2008.231
Qingfeng Chen , Deakin University, Australia
Yi-Ping Phoebe Chen , Deakin University and ARC Centre of Excellence in Bioinformatics, Australia
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.
RNA pseudoknots, stem, loop, association rule mining, PseudoBase, H-pseudoknot, function, structure, partition.
Q. Chen and Y. P. Chen, "Discovery of Structural and Functional Features in RNA Pseudoknots," in IEEE Transactions on Knowledge & Data Engineering, vol. 21, no. , pp. 974-984, 2008.