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(Turing) Individualised health-span prediction in large scale datasets using machine learning techniques

Project Description

One of the major determinants of healthy ageing is a lifestyle that includes regular exercise, a balanced diet and lack of chronic “stress”. Failure to motivate many individuals to adopt such regular social habits leads on to poor heath in later life for many groups in society.

Strategies aimed at providing information, either at a population or individual level, on risk have had some success at motivating some groups to adopt good social habits but tend to work mainly for the worried well. Broadening strategies that encourage adopting of a healthy lifestyle remains a challenge and needs complementary approaches to those based solely on risk of adverse events in the future.

An approach with merit is to show to individuals their relevant “current biology” based on their health status pre-disease. Supporting and demonstrating to individuals improvements in such relevant biology following positive lifestyle changes has great potential.

Biological profiling that underpin several important future common pre-disease states and chronic diseases are offered by measuring epigenomic and other “omic” profiles (for example measures of "biological clocks" and/or other epigenomic “signatures”). Earlier reversal of such changes can potentially be demonstrated to the end user and can also be offered as part of a wider “prevention prescription” based on promoting continued and improved future health underpinned by a measured common biology. How “omics” based assessments can best be used in this way and how it relates to measures of predisposition remain to be fully established.
Large-scale population wider datasets are needed to undertake such investigations and the UK is well served in this regard through the UK Biobank study and other cohorts to which we have access. To interpret such large and complicated data is challenging and a number of machine learning and other artificial intelligence methods have been developed that can help with such investigations.

Going forwards we believe that by putting people at the centre of their own health profile and informing and facilitating individuals to take more control of their health span. This can offer significantly greater predictive and motivational value to empower beyond what is currently delivered and allow individuals and their communities to act of such information.

Kenneth Muir:

Artitaya Lophatananon:

The Alan Turing Institute – About the studentship
The Alan Turing Institute and The University of Manchester offer a number of places each year to motivated graduate students to complete a fully funded PhD. The Turing doctoral studentship scheme combines the strengths and expertise of world-class universities with the Turing’s unique position as the UK’s national institute for data science and artificial intelligence, to offer an exceptional PhD programme.

Turing students will have access to a wide range of exceptional benefits:
• Spend time in both a university research environment and at The Alan Turing Institute.
• Access to a range of training, events, seminars, reading groups and workshops delivered by leaders in research, government and industry.
• Opportunities to collaborate on real-world projects for societal impact with current and emerging industry partners.
• Expert support and guidance through all stages of the studentship, delivered by supervisors who are Fellows of the Turing or substantively engaged with the Turing.
• Networking opportunities and brilliant minds researching a range of subjects with opportunities to collaborate and join or start interest groups.
• Opportunities to supercharge your research with access to cutting edge resources.

Find out more at

Entry Requirements
Applications are invited from UK/EU nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.

Funding Notes

Fully funded 3.5 years Studentship to commence in September 2019 under The Alan Turing Institute and The University of Manchester with a generous tax-free stipend of £20,500 per annum, a travel allowance and conference fund. If you are interested in this project, please make direct contact with the Principal Supervisor to arrange to discuss the project further as soon as possible. You MUST also submit an online application form.

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

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