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New methods to predict gene function in fission yeast (360G-Wellcome-203780_Z_16_A)

Computational biology aims to answer some of biology’s most complex questions using computational and statistical methods. The field has successfully identified genes involved in disease and has helped to discover drugs for their treatment. My PhD research takes this approach to understand the processes by which we age. Aging is a complex disease — characterised by the progressive loss of function in an organism over time — with huge social and financial cost. Faced with an aging population, breakthroughs in this area are desperately needed. I am attempting to do this using data collected from experiments that measure the lifespan of yeast in different environmental conditions. Whilst we are only distantly related to yeast, it is a useful model of human aging as it shares many cellular processes, but lives for a fraction of the time. Ultimately, I aim to use these data to predict the genes that cause yeast to age. Whilst a handful of aging genes have been identified, more genes are likely to contribute. I use networks and machine learning approaches to make my predictions. Going forward, these will help to deepen our understanding of aging and aid the development of treatments to eventually cure this disease.

£0

30 Sep 2018

Grant details
Amount Awarded 0
Applicant Surname Scholes
Approval Committee Internal Decision Panel
Award Date 2018-09-30T00:00:00+00:00
Financial Year 2017/18
Grant Programme: Title PhD Studentship (Basic)
Internal ID 203780/Z/16/A
Lead Applicant Mr Harry Scholes
Planned Dates: End Date 2020-09-30T00:00:00+00:00
Planned Dates: Start Date 2017-10-01T00:00:00+00:00
Recipient Org: Country United Kingdom
Region Greater London
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