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Bath
United Kingdom
Artificial Intelligence
Bioinformatics
Data Analysis
Data Science
Epidemiology
Genomics
Health Informatics
Neurology
Pharmacology
About the Project
The University of Bath is inviting applications for the following PhD project commencing in October 2023.
Eligible applicants will be considered for a fully-funded studentship – for more information, see the Funding Notes section below.
Supervisory Team:
Lead supervisor: Dr Prasad Nishtala (Department of Life Sciences)
Co-supervisors: Dr Olga Isupova (Department of Computer Science), Dr Sandipan Roy (Department of Mathematical Sciences) and Dr Harish Tayyar Madabushi (Department of Computer Science)
Overview of the Research:
The burden of Alzheimer’s disease (AD) hugely affects around six in every ten people with dementia in the UK. Given the difficulty of detecting and diagnosing dementia early, biomarkers are likely to play a significant role in monitoring and predicting AD. However, it remains largely vague how biomarkers in the immune system evolve from the conversion of Mild Cognitive Impairment (MCI) to clinical dementia, specifically AD. Progression to AD is postulated to be driven by the aggregation of extracellular plaques and tau protein (pTau) into neurofibrillary tangles in the brain. This pathology is accompanied by excessive brain inflammation, ultimately leading to nerve damage and dementia. There is a consensus that brain inflammation is linked to circulating biomarkers in the brain, and elevated biomarkers correlate with plaque formation and, ultimately, dementia. In this proposal, we will explore how biomarkers vary and evolve in the life course of dementia.
We will use data from the UK Biobank (UKB) or related databases containing biomarker data. UKB is a large prospective cohort of over half a million participants. For these participants, the UKB has measured a wide range of biochemical markers (n = 29), physical, sociodemographic, memory, and medical diagnosis; however, more recently, it measured in 120K participants a panel of 249 biochemical markers. The progression of patients from MCI to clinical dementia is not certain, and clinicians can’t accurately predict which people are most likely to convert. Our data will show if brain biomarkers might act as markers to facilitate the prediction of conversion from MCI or people with no dementia to clinical dementia. Our results will inform a perspective on a potential translation into clinical practice by targeting biomarkers that modulate dementia.
The novelty of this project lies in the combination of deep learning and Bayesian methods with an explicit emphasis on the mathematical foundations of both. Concretely, deep learning methods will be used to find correlations between a biomarker panel and the progression of MCI to dementia, with Bayesian methods providing uncertainty quantifications of such predictions. You will work with a highly complementary team of investigators with expertise in epidemiology, data science, artificial intelligence (AI), geriatrics and machine learning.
Project keywords: artificial intelligence, dementia, machine learning, ageing, inflammation.
Candidate Requirements:
Applicants should hold, or expect to receive, a First Class or good Upper Second Class UK Honours degree (or the equivalent) in Computer Science, Life Sciences or Mathematics. A master’s level qualification would also be advantageous.
Excellent programming skills are highly desirable, as well as experience working with Bayesian methods and deep learning, including implementing deep neural networks using, for example, PyTorch.
Non-UK applicants must meet our English language entry requirement.
Enquiries and Applications:
Applicants are encouraged to contact Dr Prasad Nishtala on email address p.nishtala@bath.ac.uk before applying to find out more about the project and to discuss their suitability for the role.
Formal applications should be made via the University of Bath’s online application form for a PhD in Pharmacy & Pharmacology.
More information about applying for a PhD at Bath may be found on our website.
Equality, Diversity and Inclusion:
We value a diverse research environment and aim to be an inclusive university, where difference is celebrated and respected. We welcome and encourage applications from under-represented groups.
If you have circumstances that you feel we should be aware of that have affected your educational attainment, then please feel free to tell us about it in your application form. The best way to do this is a short paragraph at the end of your personal statement.
Funding Notes
Candidates applying for this project may be considered for a 3.5-year Engineering and Physical Sciences Research Council (EPSRC DTP) studentship. Funding covers tuition fees, a stipend (£17,668 per annum, 2022/23 rate) and research/training expenses (£1,000 per annum). EPSRC DTP studentships are open to both Home and International students; however, in line with guidance from UK Research and Innovation (UKRI), the number of awards available to International candidates will be limited to 30% of the total.
References
1. Sudlow C, Gallacher J, Allen N, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 2015; 12: e1001779. 2015/04/01. DOI: 10.1371/journal.pmed.1001779.
2. Wang H, Yeung DY. A survey on Bayesian deep learning. ACM Computing Surveys (CSUR). 2020 Sep 28;53(5):1-37.
3. Shah, B. and Tayyar Madabushi, H., 2020, October. Efficient Brain Tumour Segmentation Using Co-registered Data and Ensembles of Specialised Learners. In International MICCAI Brainlesion Workshop (pp. 15-29). Springer, Cham.
4. Monti, R. P., Gibberd, A., Roy, S., Nunes, M., Lorenz, R., Leech, R., ... & Hyvärinen, A. (2020). Interpretable brain age prediction using linear latent variable models of functional connectivity. Plos one, 15(6), e0232296.
2. Wang H, Yeung DY. A survey on Bayesian deep learning. ACM Computing Surveys (CSUR). 2020 Sep 28;53(5):1-37.
3. Shah, B. and Tayyar Madabushi, H., 2020, October. Efficient Brain Tumour Segmentation Using Co-registered Data and Ensembles of Specialised Learners. In International MICCAI Brainlesion Workshop (pp. 15-29). Springer, Cham.
4. Monti, R. P., Gibberd, A., Roy, S., Nunes, M., Lorenz, R., Leech, R., ... & Hyvärinen, A. (2020). Interpretable brain age prediction using linear latent variable models of functional connectivity. Plos one, 15(6), e0232296.
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