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Machine Learning Automated Characterisation and Selection of Pre-Clinical Models for Experimental Design

School of Medicine, Dentistry and Biomedical Sciences | PHD

Applications are now CLOSED
Funding
Funded
Reference Number
SMED-2231-1222
Application Deadline
3 May 2023
Start Date
1 October 2023

Overview

This project will use machine learning tools to evaluate gene expression patterns in pre-clinical models of cancer in comparison to data from patients.

The use of inappropriate or inaccurate in vitro/in vivo models is a key bottleneck in the pre-clinical research and development phase. Poorly-designed experimental studies are a key risk factor in the redundant use of laboratory resources (including animals), staffing and funding. Yet, there is limited knowledge as to how accurately pre-clinical models can capture the behaviour of tumours at a macro level. This in silico project will therefore re-analyse and integrate publicly-available datasets to compare pre-clinical models with patient tumours. Using semi-supervised and machine learning techniques, pre-clinical models will be evaluated by how accuractely they replicate each cancer and by disease subtype.

Funding Information

SUBJECT TO FUNDING, AS PART OF A COMPETITIVE ROUND OF AWARDS.
Your application should clearly demonstrate your academic qualifications and relevant research experience, which will be considered by the Panel.

Project Summary
Supervisor

Dr Jaine Blayney

More Information

askmhls@qub.ac.uk

Research Profile


Mode of Study

Full-time: 3 Years


Funding Body
Department for the Economy (DfE) - Competition
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