Identifying Disease Comorbidity Patterns Using Centrality Measures in Computing

Authors

  • Zahra Batool
  • Muhammad Junaid
  • Muhammad Naeem
  • Mehmood Ahmed
  • Luqman Shah
  • Yousaf Saeed
  • Ali Imran Jehangiri
  • Fahad Ali Khan

DOI:

https://doi.org/10.47059/revistageintec.v11i4.2332

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.

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Published

2021-07-22

Issue

Section

Articles