Biomedical science is moving towards an era of personalised and stratified medicine in which the treatment that a patient receives is informed either by the patient’s individual traits (personalized medicine) or by the strata of traits to which they belong (stratified medicine). Critical to this development is the use of patient data to create and refine diagnostics that identify a patient’s disease risk and that identify optimal therapeutic strategies. At the present time there are few examples of software that specifically support the development of personalised and stratified medicine diagnostics. More fundamentally, there are currently no software libraries available that have been developed to support personalised and stratified medicine and that can simplify the development of such software.
In recent work, we have undertaken a number of analyses of large cohort patient data, combining regression techniques, computational optimisation and data discretisation in order to develop predictive diagnostics tools. In this studentship we would seek to expand on this work developing a library of functions to support diagnostics development for patient stratification that would be sufficiently general to be useable across a broad range of data types and interoperable with a broad range of programming languages.
Aims This project will develop a library of functions composed using the R scripting language to be distributed as both source code and compiled binaries to maximise compatibility across languages and platforms. The tools developed will
*support brute-force approaches, undertaking a global analysis of all permutations of subclassifications of patient data to identify the optimal subclassification. The functions will be developed to support single and multi-thread calculations and to optionally exploit parallel architectures in particular GPU architectures.
*support numerical convergent approaches that will yield approximations to optimal subclassifications in much shorter time. These functions will be developed to support single and multi-thread calculations and to optionally exploit parallel architectures in particular GPU architectures.
We will also undertake an informatics analysis of the structure of patient cohort data, using measures such as information entropy, to determine what data should be incorporated into future patient cohort studies and how it should be best exploited in order to maximise efficient identification of diagnostics.
We anticipate that the software library developed here will stimulate wide interest amongst the personalised and stratified medicine research community. It will streamline future research, improving the efficiency of diagnostic discovery. Although the software outputs will have high utility, the software library itself can stimulate incremental expansion into the future, becoming a platform for future ‘tools and resources’ research activity. Software libraries can serve as excellent promotional tools for authors and institutes into the future.
- To hold, or expect to achieve by 15 August, an Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC) in a related or cognate field.
- Sound understanding of subject area as evidenced by a comprehensive research proposal
- A comprehensive and articulate personal statement
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
- Completion of Masters at a level equivalent to commendation or distinction at Ulster
- Research project completion within taught Masters degree or MRES
- Experience using research methods or other approaches relevant to the subject domain
- Work experience relevant to the proposed project
- Publications - peer-reviewed
- Experience of presentation of research findings
The University offers the following awards to support PhD study and applications are invited from UK, EU and overseas for the following levels of support:
Vice Chancellors Research Studentship (VCRS)
Full award (full-time PhD fees + DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £15,000 maintenance grant 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.
Vice-Chancellor’s Research Bursary (VCRB)
Part award (full-time PhD fees + 50% DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £7,500 maintenance grant 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.
Vice-Chancellor’s Research Fees Bursary (VCRFB)
Fees only award (PhD fees + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees 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.
Department for the Economy (DFE)
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £15,285 per annum for three years. EU applicants will only be eligible for the fee’s component of the studentship (no maintenance award is provided). For Non-EU nationals the candidate must be "settled" in the UK. 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.
Due consideration should be given to financing your studies; for further information on cost of living etc. please refer to: www.ulster.ac.uk/doctoralcollege/postgraduate-research/fees-and-funding/financing-your-studies
The Doctoral College at Ulster University
My experience has been great and the people that I have worked with have been amazing
Kieran O'Donnell - 3D printing of biological cells for tissue engineering applicationsWatch Video
Completing the MRes provided me with a lot of different skills, particularly in research methods and lab skills.
Michelle Clements Clements - MRes - Life and Health SciencesWatch Video
Throughout my PhD I’ve been provided with continuous support and guidance by my supervisors and the staff at the University.I’ve also received many opportunities to further enhance my professional development in the form of teaching experience and presenting my work at conferences which will aid in my pursuit of a career in academia or industry.