International Journal of Management, Economics and Social Sciences
Special Issue-International Conference on Medical and Health Informatics (ICMHI 2017)
2017, Vol. 6(S1), pp. 274 – 292.
ISSN 2304 – 1366


Unveiling Disease-Protein Associations by Navigating a Structural Alphabet-Encoded Protein Network


Chi-Hua Tung1
Jih-Hsu Chang2
Jose C. Nacher3
1Dept. of Bioinformatics, Chung-Hua University, Taiwan
2Dept. of Bioinformatics, Chung-Hua University, Taiwan
3Dept. of Information Science, Toho University, Japan



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.

Keywords:  Local structure similarity network, random walk with restart, protein modularity, structural alphabet