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Identifying candidate drugs for repositioning by graph based modeling techniques based on drug side-effects

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McGarry, Kenneth, Slater, Nicole and Ammaning, Angela (2015) Identifying candidate drugs for repositioning by graph based modeling techniques based on drug side-effects. In: 15th UK Workshop on Computational Intelligence (UKCI 2015), 7 - 9 Sep 2015, University of Exeter.

Item Type: Conference or Workshop Item (Paper)

Abstract

Drug development is a lengthy and highly costly endeavor, often with limited success and high risk. The objective of drug repositioning is to apply existing drugs to different diseases or medical conditions than the original target, and thus alleviate to a certain extent the time and cost expended. The area of drug repositioning is a suitable application area for computational intelligence because numerous online databases containing technical information on drug targets, protein interactions, side-effects and biological knowledge are freely available. Thus {\it in-silico} analysis can be used as a useful first stage to screen potential candidate drugs for possible redeployment. We take the position that drugs with side-effects are potential candidates for use elsewhere, it is a case of identifying potential diseases that may benefit from this re-deployment. The system we are refining uses graph based computational techniques to analyze drugs with known side-effects and compares the proteins involved in these side-effects with proteins known to be identified with other diseases. Our intention is to find potential candidates for treating Alzheimer's disease.

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Depositing User: Kenneth McGarry

Identifiers

Item ID: 5631
URI: http://sure.sunderland.ac.uk/id/eprint/5631
Official URL: http://www.ukci2015.ex.ac.uk/

Users with ORCIDS

ORCID for Kenneth McGarry: ORCID iD orcid.org/0000-0002-9329-9835

Catalogue record

Date Deposited: 17 Jul 2015 08:07
Last Modified: 30 Sep 2020 11:03

Contributors

Author: Kenneth McGarry ORCID iD
Author: Nicole Slater
Author: Angela Ammaning

University Divisions

Faculty of Technology
Faculty of Technology > School of Computer Science

Subjects

Computing > Artificial Intelligence
Sciences > Biomedical Sciences
Computing > Databases

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  • Identifying candidate drugs for repositioning by graph based modeling techniques based on drug side-effects. (deposited 17 Jul 2015 08:07) [Currently Displayed]