This opportunity is now closed.

Funded PhD Opportunity

Leveraging Artificial Intelligence (AI) in Stratified Medicine of Multiple Myeloma.

Subject: Biomedical Sciences


Summary

Multiple Myeloma (MM) is a blood cancer, currently incurable, accounting for up to 2% of newly diagnosed cases of all malignancies per annum. It is usually preceded by more indolent phases, namely Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smouldering Multiple Myeloma (SMM) when patients are monitored, but not usually treated. Advances in treatment have improved MM survivals to 5-7 years, but upwards of 25% of patients survive for less than two years. No widely accepted biomarkers exist to identify, at presentation, patients with more progressive disease, or predict most efficacious treatments.

The aim of the PhD project is to identify biomarkers to improve prediction of disease progression and response to specific therapies in MM. Recruitment is underway to an ethically approved, longitudinal-study, with serial sampling of peripheral blood (PB) and/or bone marrow (BM) aspirate, from presentation, through to eventual relapse and development of drug resistance. To date, 91 patients have been recruited, with 238 sampling time-points. Provisional data sets include: cellular, biochemical, cytogenetic, cytokine and proteomic. Preliminary Enrichment Analysis of proteomics data has demonstrated association of ‘positive regulation of cell death’ in relapsed patients. From 2018 onwards molecular analyses on tumour cells sorted from PB and BM aspirate samples will include whole genome sequencing based gene expression profiling, variant-calling and methylation data. Using recent Artificial Intelligence (AI) based algorithms, we aim to identify biomarkers predictive of disease progression, and also the most efficacious treatment(s). We will deploy recent Deep Learning (DL) approaches, tailored for hi-dimension low-sample size (HDLS) data problems, for identifying complex patterns from multi-omics and clinical profiles of patients for stratification.

Our hypothesis is that our Artificial Intelligence (AI) based approach will enable sub-classification of patients based on disease progression and treatment response. We anticipate our computational study of big heterogeneous clinical datasets will contribute to improved patient outcomes. The use of AI algorithms that integrate clinical omics data with patient health care records will facilitate development of software applications which can be deployed in clinical diagnostic laboratories, informing treatment strategies for potential early intervention, and drug efficacy, thus contributing to the optimal management of patients. Project perfectly aligns with ongoing efforts of NI Centre for Straitfied Medicne in biomarker discovery and development of analytical tools for patients’ straitfication.

The project will be entirely computational. Thus, we are seeking a student having a strong interest in computational approaches evidenced by good programming skills (preferable in Linux/Shell, Python and R) and knowledge in biomedical sciences, computational biology and statistics. However, students from more biology oriented background but strong interest to learn bioinformatics and programming are also encouraged to apply. Appropriate training will be provided during the course of PhD study.

For any informal enquiry and/or to discuss more about the project, please contact the supervisors: Dr Priyank Shukla (p.shukla@ulster.ac.uk), Professor Denis Alexander (d.alexander@ulster.ac.uk) and Professor Tony Bjourson (aj.bjourson@ulster.ac.uk).


Essential criteria

  • Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC)
  • 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

    Vice Chancellors Research Scholarships (VCRS)

    The scholarships will cover tuition fees and a maintenance award of £14,777 per annum for three years (subject to satisfactory academic performance). Applications are invited from UK, European Union and overseas students.

    DFE

    The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 14,777 per annum for three years. EU applicants will only be eligible for the fees component of the studentship (no maintenance award is provided).  For Non EU nationals the candidate must be "settled" in the UK.


Other information


The Doctoral College at Ulster University


Reviews

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 applications

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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 Sciences

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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.

William Crowe


Key dates

Submission deadline
Monday 19 February 2018

Interview Date
6, 7 and 8 March 2018


Applying

Apply Online


Contact supervisor

Dr Priyank Shukla


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