Seyed Mehrzad Almasi
M.Sc. Candidate
Co-Supervisors: Drs. Ting Hu and Wolfgang Banzhaf

Investigation of Vertex Centralities in Human Gene-Disease Networks

Department of Computer Science
Wednesday, March 07, 2018, 3:00 p.m., Room EN 2022

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Abstract

Studying associations among genes and diseases provide an important avenue for a better understanding of genetic-related disorders and phenotypes and other complex diseases. One of the most challenging problems in biomedical research is to find the associations among genes and diseases, as well as to quantify the importance of genes. Research has shown that most human diseases cannot be attributed to a particular 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. In addition, many human genetic disorders and diseases are known to be related to each other through frequently observed co-occurrences. Studying
the correlations among multiple diseases helps us better understand the  common genetic background of diseases and develop new drugs that can treat them. Meanwhile, network science has seen an increase in applications on modeling complex biological systems, and can be a powerful tool to elucidate the correlations of multiple human diseases as well as interactions among disease genes. In this thesis, known disease-gene associations are represented using a weighted bipartite network. Subsequently, two new networks are extracted. One is a weighted human disease network from such a bipartite network to show the correlations of diseases and the other is a weighted gene network to indicate the interactions among genes. We propose a new centrality measurement designed specifically for the weighted human disease network in order to quantify the importance of diseases. Then we extend the newly proposed centrality measurement to detect the most important genes that interact with each other. We evaluate our centrality measurements and compare them with the most commonly used centralities in biological networks including degree, closeness, and betweenness. The results show that our new centrality methods are able to find more important vertices since the removal of the top-ranked vertices leads to a higher decline rate of the network efficiency.