Uncovering Disease Associations via Integration of Biological Networks
File(s)
Author(s)
Sun, Kai
Type
Thesis
Abstract
Current understanding of how diseases are associated with each other is mainly based on the similarity of clinical phenotypes. However, without considering the underlying biological mechanisms of diseases, such knowledge is limited and can even be misleading. With a growing body of transcriptomic, proteomic, metabolomic and genomic data describing diseases, we proposed to gain insights into diseases and their relationships in the light of large-scale biological data. We modelled these data as networks of inter-connected elements, and developed computational methods for their analysis.
We exploited systematic measures based on graphlets to uncover biological knowledge from network topology. Since recently some doubt had arisen concerning the applicability of graphlet-based measures to low edge density networks, we first evaluated the use of graphlet-based measures and demonstrated their suitability for biological network comparison. We also validated the use of graphlet-based measures for finding well-fitting random models for protein-protein interaction (PPI) networks, and demonstrated that five viral PPI networks are well fit by several theoretical models not previously tested.
To gain novel insights into diseases and their relationships, we integrated different types of biological data and developed computational approaches to compare diseases based on their underlying mechanisms. We applied several similarity measures including standard methods and two novel network-based measures to estimate disease association scores. We showed that disease associations predicted by our measures are correlated with associations derived from standard disease classification systems, comorbidity data, genome-wide association studies and literature co-occurrence data significantly higher than expected at random, demonstrating the ability of our measures to recover known disease associations. Furthermore, we presented case studies to validate the use of our measures in identifying previously undiscovered disease associations. We believe novel associations uncovered in our studies can enhance our knowledge of disease relationships, and may further lead to improvements in disease diagnosis and treatment.
We exploited systematic measures based on graphlets to uncover biological knowledge from network topology. Since recently some doubt had arisen concerning the applicability of graphlet-based measures to low edge density networks, we first evaluated the use of graphlet-based measures and demonstrated their suitability for biological network comparison. We also validated the use of graphlet-based measures for finding well-fitting random models for protein-protein interaction (PPI) networks, and demonstrated that five viral PPI networks are well fit by several theoretical models not previously tested.
To gain novel insights into diseases and their relationships, we integrated different types of biological data and developed computational approaches to compare diseases based on their underlying mechanisms. We applied several similarity measures including standard methods and two novel network-based measures to estimate disease association scores. We showed that disease associations predicted by our measures are correlated with associations derived from standard disease classification systems, comorbidity data, genome-wide association studies and literature co-occurrence data significantly higher than expected at random, demonstrating the ability of our measures to recover known disease associations. Furthermore, we presented case studies to validate the use of our measures in identifying previously undiscovered disease associations. We believe novel associations uncovered in our studies can enhance our knowledge of disease relationships, and may further lead to improvements in disease diagnosis and treatment.
Version
Open Access
Date Issued
2014-02
Date Awarded
2014-07
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
Advisor
Przulj, Natasa
Sponsor
GlaxoSmithKline
National Science Foundation (U.S.)
European Research Council
Grant Number
278212
OIA-1028394
Publisher Department
Computing
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)