Mining characteristic relations bind to RNA secondary structures

Chen, Qingfeng and Chen, Yi-Ping Phoebe 2010, Mining characteristic relations bind to RNA secondary structures, IEEE transactions on information technology in biomedicine, vol. 14, no. 1, pp. 10-15.

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Title Mining characteristic relations bind to RNA secondary structures
Author(s) Chen, Qingfeng
Chen, Yi-Ping Phoebe
Journal name IEEE transactions on information technology in biomedicine
Volume number 14
Issue number 1
Start page 10
End page 15
Publisher IEEE
Place of publication Piscataway, NJ
Publication date 2010-01
ISSN 1089-7771
1558-0032
Keyword(s) association group
function
H-pseudoknot
probability matrix
secondary structure
Summary The identification of RNA secondary structures has been among the most exciting recent developments in biology and medical science. It has been recognized that there is an abundance of functional structures with frameshifting, regulation of translation, and splicing functions. However, the inherent signal for secondary structures is weak and generally not straightforward due to complex interleaving substrings. This makes it difficult to explore their potential functions from various structure data. Our approach, based on a collection of predicted RNA secondary structures, allows us to efficiently capture interesting characteristic relations in RNA and bring out the top-ranked rules for specified association groups. Our results not only point to a number of interesting associations and include a brief biological interpretation to them. It assists biologists in sorting out the most significant characteristic structure patterns and predicting structurefunction relationships in RNA.
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890299 Computer Software and Services not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2010
Copyright notice ©2010, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30034415

Document type: Journal Article
Collection: School of Information Technology
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