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Conference Paper: Inferring protein-protein interactions based on sequences and interologs in Mycobacterium tuberculosis

TitleInferring protein-protein interactions based on sequences and interologs in Mycobacterium tuberculosis
Authors
Issue Date2011
Citation
The 7th International Conference on Intelligent Computing (ICIC 2011), Zhengzhou, China, 11-14 August 2011. In Lecture Notes in Computer Science, 2011, v. 6840, p. 91-96 How to Cite?
AbstractMycobacterium tuberculosis is a pathogenic bacterium that poses serious threat to human health. Inference of the protein interactions of M. tuberculosis will provide cues to understand the biological processes in this pathogen. In this paper, we constructed an integrated M. tuberculosis H37Rv protein interaction network by machine learning and ortholog-based methods. Firstly, we developed a support vector machine (SVM) method to infer the protein interactions by gene sequence information. We tested our predictors in Escherichia coli and mapped the genetic codon features underlying protein interactions to M. tuberculosis. Moreover, the documented interactions of other 14 species were mapped to the proteome of M. tuberculosis by the interolog method. The ensemble protein interactions were then validated by various functional linkages i.e., gene coexpression, evolutionary relationship and functional similarity, extracted from heterogeneous data sources. © 2012 Springer-Verlag.
Persistent Identifierhttp://hdl.handle.net/10722/222125
ISBN
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252

 

DC FieldValueLanguage
dc.contributor.authorLiu, ZP-
dc.contributor.authorWang, J-
dc.contributor.authorQiu, YQ-
dc.contributor.authorLeung, RKK-
dc.contributor.authorZhang, XS-
dc.contributor.authorTsui, SKW-
dc.contributor.authorChen, L-
dc.date.accessioned2015-12-21T06:48:41Z-
dc.date.available2015-12-21T06:48:41Z-
dc.date.issued2011-
dc.identifier.citationThe 7th International Conference on Intelligent Computing (ICIC 2011), Zhengzhou, China, 11-14 August 2011. In Lecture Notes in Computer Science, 2011, v. 6840, p. 91-96-
dc.identifier.isbn978-364224552-7-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/222125-
dc.description.abstractMycobacterium tuberculosis is a pathogenic bacterium that poses serious threat to human health. Inference of the protein interactions of M. tuberculosis will provide cues to understand the biological processes in this pathogen. In this paper, we constructed an integrated M. tuberculosis H37Rv protein interaction network by machine learning and ortholog-based methods. Firstly, we developed a support vector machine (SVM) method to infer the protein interactions by gene sequence information. We tested our predictors in Escherichia coli and mapped the genetic codon features underlying protein interactions to M. tuberculosis. Moreover, the documented interactions of other 14 species were mapped to the proteome of M. tuberculosis by the interolog method. The ensemble protein interactions were then validated by various functional linkages i.e., gene coexpression, evolutionary relationship and functional similarity, extracted from heterogeneous data sources. © 2012 Springer-Verlag.-
dc.languageeng-
dc.relation.ispartofLecture Notes in Computer Science-
dc.titleInferring protein-protein interactions based on sequences and interologs in Mycobacterium tuberculosis-
dc.typeConference_Paper-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-642-24553-4_14-
dc.identifier.scopuseid_2-s2.0-84862919883-
dc.identifier.volume6840-
dc.identifier.spage91-
dc.identifier.epage96-
dc.identifier.eissn1611-3349-
dc.customcontrol.immutablesml 160506 - amended-

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