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PhD in Dementia Research - Machine Learning for prediction in Alzheimer’s Disease: Identifying novel biologically valid diagnostic categories to inform precision medicine

  • Full or part time
    Prof V Escott-Price
  • Application Deadline
    Monday, April 08, 2019
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Employing machine learning (ML) algorithms is a promising way of exploring the complex architecture of big data in genetics. Several methods exist for solving classification problems like case-control and outcome groups in ML: support vector machines (SVMs), random forests and neural networks (NN) can all account for non-linear effects, but have different strengths and drawbacks. The student will investigate the ability of SVMs, NN and other machine learning methods for improving biologically-based classification of cases and controls in Alzheimer’s Disease (AD) and will make use the rich phenotype information available in UK Biobank to improve predictions of AD-associated outcomes and other dementia related phenotypes. Current diagnostic categories do not map onto directly underlying biology and are at odds with the continuous nature of many disease phenotypes. If we are to relate pathology to underlying mechanisms, we need to move towards constructs that are biologically valid; these are likely to stratify within, and cut across, existing diagnostic categories. There is evidence for shared genetic risk across neurodegenerative disorders and genetic strata within disorders; these have been at the vanguard of challenging existing categorical diagnostic classifications and of re-conceptualising the relationships between disorders. However, the findings do not yet point to clinical strata that are useful for predicting outcomes or treatments. Genetics only explains a portion of the risk for developing AD, so the accuracy achievable for prediction on genetics alone is limited. The set of features will therefore be expanded to include phenotype measures such as cognitive scores and life style related variables, and the best performing regression models will be compared with other frequently highly-performing classification methods, namely SVMs, random forests and neural networks. The inclusion of phenotyping measures, such as cognitive scores, to improve the models is of high interest and the best final model will likely include a combination of genetic, genomic (gene-expression), epigenetic, clinical and environmental features.

Funding Notes

The funding for the studentship is provided by the UK Dementia Research Institute at Cardiff. Funding is in competition with 2 projects advertised, and an anticipation to fund 1.
Full UK/EU tuition fees
Doctoral stipend matching UK Research Council National Minimum.
Additional funding is available over the course of the programme and will cover costs such as research consumables and training.

Applicants should possess a minimum of an upper second class Honours degree, master's degree, or equivalent in a relevant subject.
Applicants whose first language is not English are normally expected to meet the minimum University requirements (e.g. 6.5 IELTS)


The duration of the PhD will be 3 years.
In order to be considered you must submit a formal application via Cardiff University’s online application service. (To access the system click 'Apply Online' at the bottom of this advert)
There is a box at the top right of the page labelled ‘Apply’, please ensure you select the correct ‘Qualification’ (Doctor of Philosophy), the correct ‘Mode of Study’ (Full Time) and the correct ‘Start Date’ (October 2019). This will take you to the application portal.
In order to be considered candidates must submit the following information:

• Supporting statement
• CV
• Qualification certificates
• References x 2 (references are optional but will strengthen your application)
• Proof of English language (if applicable)

In the 'Research proposal and Funding' section of your application, please specify the project title and supervisors of the project and copy the project description in the text box provided.
Please select 'No, I am not self-funding my research' when asked whether you are self-funding your research.
In the funding section, please select "I will be applying for a scholarship / grant" and specify that you are applying for advertised funding from UK DRI.

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