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Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

A method to explore the connectivity patterns of proteins and drugs for identifying disease communities

McGarry, Kenneth, Nelson, David and Ashton, Mark (2020) A method to explore the connectivity patterns of proteins and drugs for identifying disease communities. SN Computer Science, 137. ISSN 2661-8907

Item Type: Article


Diseases are often caused by defective proteins, these proteins rarely operate in isolation and may have several roles in the cell. Thus over time a defective protein may be involved in several disorders, either directly or indirectly. The multiple roles leads to the concept of a disease module or cluster. This work describes how we generate overlapping clusters from complex networks to explore the dynamic nature of diseases, the genes implicated with them and the drugs used to treat them. Link clustering is vital for community detection as it enables the integration of disparate sources of data and provides a better understanding of community hierarchy and community dynamics than non-link methods. Furthermore, we view not just the genes directly shared between diseases but also indirectly connected genes in the network neighborhood. We use data and information from the STITCH protein and drug interaction databases, OMIM disease database, lists of diseases categorized by MeSH and the drugbank repository. The Gene Ontology, Disease Ontology and KEGG provide biological validity for the disease communities. We demonstrate how the detection of overlapping clusters enables the identification of biologically plausible communities consisting of cooperating proteins. We verify their role in disease with respect to targeting drugs more effectively with expert opinion. We have been able to identify various modules that make sense from a biological and medical perspective and validate drug repositioning candidates with

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More Information

Uncontrolled Keywords: Link clustering, gene ontology, MeSH, disease modules
Depositing User: Kenneth McGarry


Item ID: 11905
Identification Number:
ISSN: 2661-8907
Official URL:

Users with ORCIDS

ORCID for Kenneth McGarry: ORCID iD

Catalogue record

Date Deposited: 06 Apr 2020 06:59
Last Modified: 03 Apr 2021 02:38


Author: Kenneth McGarry ORCID iD
Author: David Nelson
Author: Mark Ashton
Author: Kenneth McGarry
Author: David Nelson
Author: Mark Ashton

University Divisions

Faculty of Technology > School of Computer Science


Computing > Data Science
Computing > Artificial Intelligence
Sciences > Biomedical Sciences
Computing > Databases

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