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Details of Grant 

EPSRC Reference: EP/K008250/1
Title: An Active Learning Approach to Network Inference
Principal Investigator: Campbell, Dr C
Other Investigators:
Murphy, Professor D
Researcher Co-Investigators:
Project Partners:
Department: Engineering Mathematics and Technology
Organisation: University of Bristol
Scheme: Standard Research
Starts: 01 July 2013 Ends: 31 October 2016 Value (£): 268,375
EPSRC Research Topic Classifications:
Artificial Intelligence Bioinformatics
EPSRC Industrial Sector Classifications:
Healthcare
Related Grants:
Panel History:
Panel DatePanel NameOutcome
09 Oct 2012 EPSRC ICT Responsive Mode - Oct 2012 Announced
Summary on Grant Application Form
The understanding of pathway structures is crucial to our understanding of the functional organisation of genes and proteins. Abnormally functioning pathways underlie many human diseases. Given the extent of noise in biological datasets, and limited amounts of data available, unsupervised determination of network topology is a substantially under-determined inference problem. Consequently, in this project, we focus on network completion, which is a supervised learning task involving multiple types of data. Starting from a set of known links and non-links we construct a classifier which predicts and ranks further possible functionmal links for experimental validation. There will be a parallel experimental programme following an active learning approach to network inference, that is, predictions of functional links will be investigated which will, in turn, be used to improve the predictor. To provide a focus for the accompanying laboratory work, by the medical researchers associated with the project, our principal aim will be the discovery and validation of pathway structures associated with hypertension. Hypertension is the most common cause of preventable disease in the developed world.
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Further Information:  
Organisation Website: http://www.bris.ac.uk