Investigation of vertex centralities in human gene-disease networks

Almasi, Seyed Mehrzad (2018) Investigation of vertex centralities in human gene-disease networks. Masters thesis, Memorial University of Newfoundland.

[img] [English] PDF - Accepted Version
Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission.

Download (6MB)


Studying associations among genes and diseases provides an important avenue for a better understanding of genetic-related disorders, phenotypes and other complex diseases. Research has shown that many complex 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 to 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 more effectively and avoid side effects. Meanwhile, network science has seen an increase in applications to model complex biological systems, and can be a powerful tool to elucidate the correlations of multiple human diseases as well as interactions among associated genes. In this thesis, known disease-gene associations are represented using a weighted bipartite network. Subsequently, two new networks are extracted. One is the weighted human disease network to show the correlations of diseases, and the other is the weighted gene network to capture the interactions among genes. We propose two new centrality measures for the weighted human disease network and the weighted gene network. 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 can find more important vertices since the removal of the top-ranked vertices leads to a higher decline rate of the network efficiency. Our identified key diseases and genes hold the potential of helping better understand the genetic background and etiologies of complex human diseases.

Item Type: Thesis (Masters)
Item ID: 13336
Additional Information: Includes bibliographical references (pages 83-99).
Keywords: Complex networks, Bipartite graphs, Weighted networks, Vertex importance, Vertex centrality, Human disease network, Disease-gene association, Human gene network
Department(s): Science, Faculty of > Computer Science
Date: May 2018
Date Type: Submission
Library of Congress Subject Heading: Genetic disorders--Mathematical models; System analysis; Centrality (Graph theory)

Actions (login required)

View Item View Item


Downloads per month over the past year

View more statistics