PhD Study : How to optimally identify patient strata: tools and technologies for managing data sets so as to yield patient classification systems.

Summary

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.

Impact

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.

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.

• Sound understanding of subject area as evidenced by a comprehensive research proposal
• A comprehensive and articulate personal statement

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
• 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

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