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J Biomed Inform. 2014 Feb;47:178-91. doi: 10.1016/j.jbi.2013.10.011. Epub 2013 Nov 5.

Structural network analysis of biological networks for assessment of potential disease model organisms.

Author information

1
Center for Clinical & Translational Science, University of Vermont, Burlington, VT, USA; Department of Computer Science, University of Vermont, Burlington, VT, USA; Faculty of Computers & Information, Fayoum University, Al Fayoum, Egypt.
2
Center for Clinical & Translational Science, University of Vermont, Burlington, VT, USA; Department of Microbiology & Molecular Genetics, University of Vermont, Burlington, VT, USA; Department of Computer Science, University of Vermont, Burlington, VT, USA. Electronic address: neil.sarkar@uvm.edu.

Abstract

Model organisms provide opportunities to design research experiments focused on disease-related processes (e.g., using genetically engineered populations that produce phenotypes of interest). For some diseases, there may be non-obvious model organisms that can help in the study of underlying disease factors. In this study, an approach is presented that leverages knowledge about human diseases and associated biological interactions networks to identify potential model organisms for a given disease category. The approach starts with the identification of functional and interaction patterns of diseases within genetic pathways. Next, these characteristic patterns are matched to interaction networks of candidate model organisms to identify similar subsystems that have characteristic patterns for diseases of interest. The quality of a candidate model organism is then determined by the degree to which the identified subsystems match genetic pathways from validated knowledge. The results of this study suggest that non-obvious model organisms may be identified through the proposed approach.

KEYWORDS:

Disease pathway mining; Interaction networks; Structural pattern analysis; Translational bioinformatics

PMID:
24211613
DOI:
10.1016/j.jbi.2013.10.011
[Indexed for MEDLINE]
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