Volume 11 - Volume 11
Identifying Disease Comorbidity Patterns Using Centrality Measures in Computing
Abstract
Social network analysis has been increasingly employed to study patterns in diverse areas of
disciplines such as crowd management, air passenger and freight transportation, business modelling
and analysis, online social movements and bioinformatics. Over the years, human disease networks
have been studied to analyze Human Disease, Genotype, and Phenotype networks. This study
explores human Disease Network based on their symptoms by employing different social network
analysis such as centrality measures of network, community detection, overlapping communities. We
studied relationships of symptoms with diseases on meso-level in order to detect comorbidity pattern
of communities in disease network. This help us to understand the underlying patterns of diseases
based on symptoms and find out that how different disease communities are correlated by detecting
overlapping communities.
Paper Details
PaperID: 2332
Author's Name: Zahra Batool, Muhammad Junaid, Muhammad Naeem, Mehmood Ahmed, Luqman Shah, Yousaf Saeed, Ali Imran Jehangiri and Fahad Ali Khan
Volume: Volume 11
Issues: Volume 11
Keywords: Network Analysis, Disease Network, Disease Association, Centrality Measures, Community Detection, Meso Level.
Year: 2021
Month: July
Pages: 2964-2975