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Complex network based computational techniques for edgetic modelling of mutations implicated with human diseases

McGarry, Kenneth, Emery, Kirsty, Varnakulasingam, Vithusa, McDonald, Sharon and Ashton, Mark (2016) Complex network based computational techniques for edgetic modelling of mutations implicated with human diseases. In: 16th UK Workshop on Computational Intelligence, UKCI-2016, 7-9 Sep 2016, Lancaster University.

Item Type: Conference or Workshop Item (Paper)


Complex networks are a graph theoretic method that can model genetic mutations, in particular single nucleotide polymorphisms (snp’s) which are genetic variations that only occur at single position in a DNA sequence. These can potentially cause the amino acids to be changed and may affect protein function and thus structural stability which can contribute to developing diseases. We show how snp’s can be represented by complex graph structures, the connectivity patterns if represented by graphs can be related to human diseases, where the proteins are the nodes (vertices) and the interactions between them are represented by links (edges). Disruptions caused by mutations can be explained as loss of connectivity such as the deletion of nodes or edges in the network (hence the term edgetics). Furthermore, diseases appear to be interlinked with hub genes causing multiple problems and this has led to the concept of the human disease network or diseasome. Edgetics is a relatively new concept which is proving effective for modelling the relationships between genes, diseases and drugs which were previously considered intractable problems.

UKCI2016A_McGarry.pdf - Accepted Version

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Depositing User: Kenneth McGarry


Item ID: 6501
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ORCID for Kenneth McGarry: ORCID iD

Catalogue record

Date Deposited: 01 Sep 2016 08:42
Last Modified: 18 Dec 2019 15:39


Author: Kenneth McGarry ORCID iD
Author: Kirsty Emery
Author: Vithusa Varnakulasingam
Author: Sharon McDonald
Author: Mark Ashton

University Divisions

Faculty of Technology
Faculty of Technology > FOT Executive
Faculty of Technology > School of Computer Science


Computing > Artificial Intelligence
Sciences > Health Sciences

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