Interval-Based Similarity for Classifying Conserved RNA Secondary Structures

Chen, Q; Chen, YPP; Zhang, C

Interval-Based Similarity for Classifying Conserved RNA Secondary Structures

Chen, Q Chen, YPP Zhang, C

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Publication Type:: Journal Article
Citation:: IEEE Intelligent Systems, 2016, 31 (3), pp. 78 - 85
Issue Date:: 2016-05-01

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Field	Value	Language
dc.contributor.author	Chen, Q	en_US
dc.contributor.author	Chen, YPP	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2016-05-01	en_US
dc.identifier.citation	IEEE Intelligent Systems, 2016, 31 (3), pp. 78 - 85	en_US
dc.identifier.issn	1541-1672	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122892
dc.description.abstract	© 2001-2011 IEEE. The structure of a molecule is critical to determine its function in a biological context. Intelligent strategies are required to group structures with high similarity in an intuitive way. Most previous approaches focus on addressing sequence similarity and gene expression, whereas the techniques for comparative analysis of conserved structure data are underdeveloped because various secondary structures are complex, and most existing distance metrics have limitations. This article proposes a novel classification schema in terms of interval-based and weighted similarity functions that considers the intersection, non-intersection, and inclusion between two intervals of sequence size for conserved structures. The secondary structures are characterized by distance vectors. This assists in classifying structures under specific structure patterns that are expected to be correlated by functional or structural importance.	en_US
dc.relation.ispartof	IEEE Intelligent Systems	en_US
dc.relation.isbasedon	10.1109/MIS.2015.2	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Interval-Based Similarity for Classifying Conserved RNA Secondary Structures	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	31	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	31	en_US

Abstract:

© 2001-2011 IEEE. The structure of a molecule is critical to determine its function in a biological context. Intelligent strategies are required to group structures with high similarity in an intuitive way. Most previous approaches focus on addressing sequence similarity and gene expression, whereas the techniques for comparative analysis of conserved structure data are underdeveloped because various secondary structures are complex, and most existing distance metrics have limitations. This article proposes a novel classification schema in terms of interval-based and weighted similarity functions that considers the intersection, non-intersection, and inclusion between two intervals of sequence size for conserved structures. The secondary structures are characterized by distance vectors. This assists in classifying structures under specific structure patterns that are expected to be correlated by functional or structural importance.

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http://hdl.handle.net/10453/122892