Document Detail

Interval-based distance function for identifying RNA structure candidates.
MedLine Citation:
PMID:  21056578     Owner:  NLM     Status:  In-Data-Review    
Many clustering approaches have been developed for biological data analysis, however, the application of traditional clustering algorithms for RNA structure data analysis is still a challenging issue. This arises from the existence of complex secondary structures while clustering. One of the most critical issues of cluster analysis is the development of appropriate distance measures in high dimensional space. The traditional distance measures focus on scale issues, but ignores the correlation between two values. This article develops a novel interval-based distance (Hausdorff) measure for computing the similarity between characterized structures. Three relationships including perfect match, partially overlapped and non-overlapped are considered. Finally, we demonstrate the methods by analyzing a data set of RNA secondary structures from the Rfam database.
Qingfeng Chen; Gang Li; Yi-Ping Phoebe Chen
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Publication Detail:
Type:  Journal Article     Date:  2010-11-05
Journal Detail:
Title:  Journal of theoretical biology     Volume:  269     ISSN:  1095-8541     ISO Abbreviation:  J. Theor. Biol.     Publication Date:  2011 Jan 
Date Detail:
Created Date:  2010-12-21     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0376342     Medline TA:  J Theor Biol     Country:  England    
Other Details:
Languages:  eng     Pagination:  280-6     Citation Subset:  IM    
Copyright Information:
Copyright © 2010 Elsevier Ltd. All rights reserved.
School of Computer, Electronic and Information, Guangxi University, Nanning 530004, China; Department of Computer Science and Computer Engineering, La Trobe University, VIC 3086, Australia.
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