Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/173242
Authors: 
Tung, Chi-Hua
Chang, Jih-Hsu
Nacher, Jose C.
Year of Publication: 
2017
Citation: 
[Journal:] International Journal of Management, Economics and Social Sciences (IJMESS) [ISSN:] 2304-1366 [Volume:] 6 [Year:] 2017 [Issue:] Special Issue [Pages:] 274-292
Abstract: 
The identification of genes associated with human disorders is a major goal in computational biology. Although the rapid emergence of cellular network-based approaches has been successful in many instances, all of these methodologies are partially limited by the incompleteness of the interactome. Here, we propose a novel method that may overcome the inherent problem of these incomplete molecular networks and assist already established network techniques. Instead of using protein-protein interaction networks, we encode the local threedimensional structure of a protein into a series of letters, called the Structural Alphabet, and define a proteomic structural network in which each node represents a unit of the structural alphabet (USA) and each pair of USAs is linked based on their structural similarity. This novel structural network is the platform by which a diffusion-based algorithm determines the potential involvement of proteins in disease phenotypes. Computational experiments show that the combination of diffusion-based methods with the constructed structural alphabet network offers better predictive performance than the results obtained using interactome networks and provides a new avenue to assist in identifying disease-related proteins.
Subjects: 
Local structure similarity network
random walk with restart
protein modularity
structural alphabet
Creative Commons License: 
http://creativecommons.org/licenses/by-nc/3.0/
Document Type: 
Article

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