Exploring Consensus RNA Substructural Patterns Using Subgraph Mining

Chen, Q; Lan, C; Chen, B; Wang, L; Li, J; Zhang, C

Exploring Consensus RNA Substructural Patterns Using Subgraph Mining

Chen, Q Lan, C Chen, B Wang, L Li, J

Zhang, C

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Publication Type:: Journal Article
Citation:: IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2017, 14 (5), pp. 1134 - 1146
Issue Date:: 2017-09-01

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Field	Value	Language
dc.contributor.author	Chen, Q	en_US
dc.contributor.author	Lan, C	en_US
dc.contributor.author	Chen, B	en_US
dc.contributor.author	Wang, L	en_US
dc.contributor.author	Li, J https://orcid.org/0000-0003-1833-7413	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2017-09-01	en_US
dc.identifier.citation	IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2017, 14 (5), pp. 1134 - 1146	en_US
dc.identifier.issn	1545-5963	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124721
dc.description.abstract	© 2017 IEEE. Frequently recurring RNA ?> structural motifs play important roles in RNA folding process and interaction with other molecules. Traditional index-based and shape-based schemas are useful in modeling RNA secondary structures but ignore the structural discrepancy of individual RNA family member. Further, the in-depth analysis of underlying substructure pattern is insufficient due to varied and unnormalized substructure data. This prevents us from understanding RNAs functions and their inherent synergistic regulation networks. This article thus proposes a novel labeled graph-based algorithm RnaGraph to uncover frequently RNA substructure patterns. Attribute data and graph data are combined to characterize diverse substructures and their correlations, respectively. Further, a top-k graph pattern mining algorithm is developed to extract interesting substructure motifs by integrating frequency and similarity. The experimental results show that our methods assist in not only modelling complex RNA secondary structures but also identifying hidden but interesting RNA substructure patterns.	en_US
dc.relation.ispartof	IEEE/ACM Transactions on Computational Biology and Bioinformatics	en_US
dc.relation.isbasedon	10.1109/TCBB.2016.2645202	en_US
dc.subject.classification	Bioinformatics	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	RNA	en_US
dc.subject.mesh	Sequence Analysis, RNA	en_US
dc.subject.mesh	Computational Biology	en_US
dc.subject.mesh	Consensus Sequence	en_US
dc.subject.mesh	Nucleic Acid Conformation	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Pattern Recognition, Automated	en_US
dc.subject.mesh	Data Mining	en_US
dc.title	Exploring Consensus RNA Substructural Patterns Using Subgraph Mining	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	14	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0604 Genetics	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	06 Biological Sciences	en_US
utslib.for	08 Information and Computing Sciences	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/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
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
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	14	en_US

Abstract:

© 2017 IEEE. Frequently recurring RNA ?> structural motifs play important roles in RNA folding process and interaction with other molecules. Traditional index-based and shape-based schemas are useful in modeling RNA secondary structures but ignore the structural discrepancy of individual RNA family member. Further, the in-depth analysis of underlying substructure pattern is insufficient due to varied and unnormalized substructure data. This prevents us from understanding RNAs functions and their inherent synergistic regulation networks. This article thus proposes a novel labeled graph-based algorithm RnaGraph to uncover frequently RNA substructure patterns. Attribute data and graph data are combined to characterize diverse substructures and their correlations, respectively. Further, a top-k graph pattern mining algorithm is developed to extract interesting substructure motifs by integrating frequency and similarity. The experimental results show that our methods assist in not only modelling complex RNA secondary structures but also identifying hidden but interesting RNA substructure patterns.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/124721