MRC Precision Medicine DTP: Applying machine learning models to genome data to understand the evolution of resistance from virus to cancer evolution

Supervisors:
Prof David L Robertson
Dr Ke Yuan

Project summary:
Treatment of cancer and chronic infectious diseases often fail due to the evolution of resistance to therapy. Underpinning this phenomena is the generation of changes (mutations) in their genetic material. Mutations generate high levels of differences in the genomes of cancer cells or intra-patient virus populations that leads to their ability to evolve in response to drugs. Recent advances in genome sequencing have revealed genomic alterations that drive cancer progression and pathogen infection. These data give insight into the diseases’ underlying evolutionary dynamics which follow predictions of both Darwin’s theory of evolution and Motoo Kimura’s theory of molecular evolution. Yet, how evolutionary dynamics interact with mutational processes, and whether these processes can predict clinical outcome is largely unknown. Due to the variety and complexity of genomic alterations observed across human, cancer and virus evolution, unified mathematical equations of evolution are often intractable. We propose to leverage state of the art machine learning methods applied to large scale genome sequencing data sets to build biologically informed data-driven models of evolutionary dynamics. These models permit efficient data analysis that account for the variety and complexity of genomic alterations observed across human, cancer and virus evolution. They will infer the life histories of disease processes and predict disease progression and effects of interventions. Early prediction of resistance to therapies is essential to maximising the potency of interventions and switching treatments when necessary. The student will be trained in a combination of data science and bioinformatics, with substantial elements of computation, programming and statistics/machine learning.

This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

All applications should be made via the University of Edinburgh, irrespective of project location. Please note that your application along with any supporting documents will be shared with between both the University of Edinburgh and Glasgow.

For information on ‘How to Apply’ is detailed here: https://www.gla.ac.uk/colleges/mvls/graduateschool/precisionmedicine/howtoapply/
Please note that all applications must be submitted via the University of Edinburgh: http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=919

Please note, you must apply to one of the projects and you must contact the primary supervisor prior to making your application. Additional information on the application process is available from the link above.

For more information about Precision Medicine visit: http://www.ed.ac.uk/usher/precision-medicine