PhD Study : Artificial intelligence for analyzing heterogeneity in Alzheimer's disease and its prodromal stages: Identifying novel diagnostic categories to inform precision medicine

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Summary

Alzheimer’s disease (AD) is the leading cause of functional dependence and disability among the elderly in the UK, creating enormous cost to the wider economy (£26bn/year). Considering recent setbacks in drug development for AD, we urgently need new approaches for disease prevention and treatment. In recent years, the emerging field of precision medicine has been gradually replacing the traditional “one-size-fits-all” medical approach to prevention, diagnosis, and treatment of diseases. It is now agreed that two patients with the same disease may present with different symptoms, progress at different rates, and respond differently to the same therapy.

This is especially important for complex disorders with multiple cognitive and behavioural symptoms, such as AD and Mild Cognitive Impairment (MCI, prodromal stage of AD) in which patients exhibit different patterns of disease progression and therapeutic responses. Finding the link between these disease phenotypes and individual’s clinical, biological, and behavioural characteristics is a complex task and requires the use of sophisticated computational approaches. In this study, we propose to apply stratified medicine approaches to MCI/AD characterization and develop a computational framework to analyse patient heterogeneity in the prodromal stage and progression of AD.

The specific aims of this study include:

1.To investigate individual variability in genes and lifestyle characteristics and combine them with molecular, environmental, and behavioural factors that may contribute to the accelerated onset and progression of Alzheimer’s disease.

2.To apply state-of-art machine learning methods for improving biologically-based classification of MCI and AD cases.

3.To develop computational models of disease progression using probabilistic (Hidden Markov Model, General Mixture models) and deep learning (recurrent auto-encoder) approaches to identify novel diagnostic disease categories.

4.To apply the gene expression connectivity mapping technology to computationally screen a large collection of approved drugs to identify their potential new use for treatment of stratified subgroup(s) of AD patients.

We will utilise the data sets from Dementias Platform UK (DPUK), the world’s richest source of data for use in research and drug development for dementia, and the in-house AD cohort from the NI Centre for Stratified Medicine. The data will be used to create highly detailed and individualized profiles of both MCI and AD states, which will in turn be used to understand multi-determinant causal relationships, elucidate modifiable factors, and ultimately customize treatments based on individual parameters. In the case of AD, where many questions are still unanswered and effective therapies have remained elusive, there is a critical need for an interdisciplinary perspective on data and information management to derive novel insights, generate new knowledge, and facilitate treatment discovery.

The supervisory team have complementary skills and specialized expertise required for successful completion of this project. The team have previously collaborated successfully across interdisciplinary projects. The in-depth knowledge of the structure and content of different DPUK datasets (45 different cohort studies, 3 million+ participants) and familiarity with the data access approval process (Dr Sarah Bauermeister) will lead to prompt identification of suitable cohorts. In addition, the PhD candidate will receive ongoing support via DPUK Reach.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

  • Research proposal of 1500 words detailing aims, objectives, milestones and methodology of the project
  • A demonstrable interest in the research area associated with the studentship

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

  • First Class Honours (1st) Degree
  • Masters at 70%
  • For VCRS Awards, Masters at 75%
  • Publications - peer-reviewed
  • Experience of presentation of research findings
  • Applicants will be shortlisted if they have an average of 75% or greater in a first (honours) degree (or a GPA of 8.75/10). For applicants with a first degree average in the range of 70% to 74% (GPA 3.3): If they are undertaking an Masters, then the average of their first degree marks and their Masters marks will be used for shortlisting.

Funding and eligibility

The University offers the following levels of support:

Vice Chancellors Research Studentship (VCRS)

The following scholarship options are available to applicants worldwide:

  • Full Award: (full-time tuition fees + £19,000 (tbc))
  • Part Award: (full-time tuition fees + £9,500)
  • Fees Only Award: (full-time tuition fees)

These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG) to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Please note: you will automatically be entered into the competition for the Full Award, unless you state otherwise in your application.

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

  • Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
  • Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
  • Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
  • Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living

The Doctoral College at Ulster University

Key dates

Submission deadline
Friday 5 February 2021
12:00AM

Interview Date
25 March 2021

Preferred student start date
mid September 2021

Applying

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Other supervisors

  • Dr Shu-Dong Zhang
  • Dr Sarah Bauermeister, Dementias Platform UK, Medical Sciences Division, University of Oxford