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Details of Grant 

EPSRC Reference: GR/R96255/01
Title: Machine Learning Techniques for Functional Interpretation of Gene Expression Data from cDNA Microarrays (Overseas Travel Grant application)
Principal Investigator: Campbell, Dr I
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Engineering Mathematics
Organisation: University of Bristol
Scheme: Overseas Travel Grants Pre-FEC
Starts: 19 November 2002 Ends: 18 November 2005 Value (£): 12,393
EPSRC Research Topic Classifications:
Artificial Intelligence Bioinformatics
Genomics
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:  
Summary on Grant Application Form
The recent development of microarray technology is rapidly accelerating progress in many areas of biomedical research. For the first time this technology gives a global view of the expression levels of thousands of genes. Already this approach has lead to significant discoveries such as subclusters in gene expression datasets for certain cancers, pointing to a new genetic taxonomy for these diseases. Data analysis frequently utilizes machine learning techniques including cluster analysis, classification and feature selection, outlier detection and regression, for example. The grant would be used to strengthen a pre-existing collaborative links with MIT, who currently have a major interest in generation of microarray datasets and the development of algorithmic and theoretical techniques for analysis of such datasets. Currently the Principal Investigator is working with a number of UKbased experimental groups generating microarray datasets (mainly for cancer). However, it is also very important to also innovate analytical techniques to make a maximum gain from these datasets.
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Organisation Website: http://www.bris.ac.uk