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

Performing trend analysis on spatio-temporal proteomics data using differential ratio data mining

Malone, James, McGarry, Kenneth and Bowerman, Chris (2004) Performing trend analysis on spatio-temporal proteomics data using differential ratio data mining. In: Proceedings of the 6th EPSRC Conference on Postgraduate Research in Electronics, Photonics, Communications and Software (PREP 2004), 5-7 Apr 2004.

Item Type: Conference or Workshop Item (Poster)


Differential Ratio data mining was used to perform knowledge discovery within the 2-DE proteomics data, incorporating the spatial and temporal components. How does the work advance the state-of-the-art?: Development of data mining technique that performs automatic discovery of interesting trends within large spatio-temporal data incorporating both spatial and temporal elements, and nonspatial/temporal elements that describe the data. A measure is also introduced to evaluate and rank discoveries. Motivation (problems addressed): Analysis of 2-DE proteomics data is presently undertaken manually since current automated techniques are unable to identify interesting trends successfully, due to the inability to incorporate the spatial and temporal elements. An automated knowledge discovery technique could prove very useful within important areas of proteomics research and other spatio-temporal datasets.

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More Information

Depositing User: Kenneth McGarry


Item ID: 5282
Official URL:

Users with ORCIDS

ORCID for Kenneth McGarry: ORCID iD

Catalogue record

Date Deposited: 06 Mar 2015 10:49
Last Modified: 18 Dec 2019 15:37


Author: Kenneth McGarry ORCID iD
Author: James Malone
Author: Chris Bowerman

University Divisions

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


Computing > Artificial Intelligence
Sciences > Biomedical Sciences

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