Paper Info
Title
Inferring protein-protein interactions based on sequences and interologs in mycobacterium tuberculosis
Abstract
Mycobacterium 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.
Year
DOI
Venue
2011
10.1007/978-3-642-24553-4_14
ICIC
Keywords
Field
DocType
gene coexpression,m. tuberculosis,protein-protein interaction,mycobacterium tuberculosis,h37rv protein interaction network,gene sequence information,functional similarity,underlying protein interaction,integrated m. tuberculosis,protein interaction,ensemble protein interaction
Mycobacterium tuberculosis,Protein–protein interaction,Gene,Computer science,Artificial intelligence,Computational biology,Escherichia coli,Interaction network,Proteome,Bioinformatics,Tuberculosis,Machine learning,Pathogen
Conference
Volume
ISSN
Citations 
6840
0302-9743
0
PageRank 
References 
Authors
0.34
5
7
Name
Order
Citations
PageRank
Zhi-Ping Liu11008.99
J. Wang2193.32
Yu-Qing Qiu3404.54
Ross K. K. Leung4101.48
Xiang-Sun Zhang5101677.06
Stephen K.-W. Tsui613012.70
Luonan Chen71485145.71