Unveiling disease-protein associations by navigating a structural alphabet-encoded protein network

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: International Journal of Management, Economics and Social Sciences (IJMESS) ; ISSN: 2304-1366 ; Volume: 6 ; Year: 2017 ; Issue: Special Issue ; Pages: 274-292 ; Jersey City, NJ: IJMESS International Publishers

Classification and Topics

• Classification: Wirtschaft
• Subject: Local structure similarity network ; random walk with restart ; protein modularity ; structural alphabet

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Tung, Chi-Hua ; Chang, Jih-Hsu ; Nacher, Jose C.
• Event: Veröffentlichung
• (who): IJMESS International Publishers
• (where): Jersey City, NJ
• (when): 2017

Further information

• Handle: http://hdl.handle.net/10419/173242
• Last update: 10.03.2025, 11:42 AM CET