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.

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Abstract

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.

Item Type:	Conference or Workshop Item (Poster)
Subjects:	Computing > Artificial Intelligence Sciences > Biomedical Sciences
Divisions:	Faculty of Technology Faculty of Technology > School of Computer Science Faculty of Technology > FOT Executive
Depositing User:	Kenneth McGarry
Date Deposited:	06 Mar 2015 10:49
Last Modified:	18 Dec 2019 15:37
URI:	http://sure.sunderland.ac.uk/id/eprint/5282

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