Seminar: Investigation of Vertex Centralities in Human Gene-Disease Networks

Seyed Mehrzad Almasi
M.Sc. Candidate
Supervisor: Dr. Ting Hu

Investigation of Vertex Centralities in Human Gene-Disease Networks

Department of Computer Science
Friday, August 4, 2017, 11:00 a.m., Room EN 2022


Abstract

Studying associations among genes and diseases provide an important avenue for better understanding of genetic-related disorders, complex diseases and genetic-related phenotypes. One of the most challenging problems in biomedical research is to find the associations among genes and diseases, as well as quantifying the importance of genes. Research has shown that most human diseases cannot be attributed to a special gene, but a set of interacting genes. The effect of a specific gene on multiple diseases is called Pleiotropy and interactions among several genes to contribute a specific disease is called Epistasis. A network-based analysis is one of the best ways for indicating the associations among genes and diseases where the vertices in the network refer to both genes and diseases. In this project, a bipartite graph will be used to show the associations among genes and diseases. Subsequently, two new networks will be extracted based on the bipartite graph; One of them is to study the interactions among genes and the next one is to study the correlations of diseases. Several basic centrality measures such as degree distribution, and closeness centrality will be used to identify important genes and diseases. In addition, some new measures proposed recently will be applied and the results will be compared with the results given by basic centrality measures. We expect that by applying some extensions on existing vertex centralities, the centrality measures quantify genes and diseases importance in a more accurate way. In addition, the proposed methods in this project is expected to resolve the probable weaknesses of the existing methods proposed by other studies.

 

Contact

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