Funded PhD Opportunity Personalized medicine using computational analysis of omics data and molecular interaction networks.
This opportunity is now closed.
Subject: Biomedical Sciences
This project will use computational approaches to improve our ability to use big ‘omics’ datasets to understand differences between patient groups with respect to disease mechanism, disease progression, and drug-response. Stratified medicine (or personalized/precision medicine) depends on the identification of biological features that can be used to separate a patient population into sub-groups, to enable biomarker identification and therapeutic development. “Omics” methodologies such as whole genome or whole transcriptome sequencing, microarrays, and proteomics, are often employed to identify useful biological features, because their high-throughput nature enables the quantification of thousands of features in a single experiment.
One approach to stratification is to apply machine learning / artificial intelligence based algorithms to omics data generated from a large patient cohort in order to build a mathematical model that can correctly assign sub-group identity based on the most predictive biological features. However, this approach ignores another potentially important source of information: the network of known functional relationships between biological molecules. These may take many forms, including gene co-expression, protein-protein binding, miRNA-target and transcription factor to target interactions. Clustering analysis of functional association networks, in which the structural topology of the network is computationally analyzed to identify tightly interacting groups of molecules, has been successfully applied to diverse pathologies including cancer, cardiovascular disease, type 2 diabetes, asthma, and schizophrenia.
The central hypothesis of this project will be that our capacity to stratify a patient population based on high-throughput molecular data is improved by the inclusion of functional association data. At our disposal to test this are: omics datasets and functional association data from public repositories; and in-house datasets from several disease areas currently under study at the Northern Ireland Centre for Stratified Medicine (NICSM).
The candidate will develop computational method(s), which will involve: first to score network clusters based on the extent to which they are impacted at the molecular level in terms of genetic variants or differential expression, followed by deploying (training and testing) various machine learning algorithms to identify clusters whose scores are predictive of a patient’s disease sub-group. Success in the project will feed into the ongoing efforts of the NICSM in these disease areas, focused on biomarker discovery in the short term and therapeutic development in the longer term. In practical terms, the project is intended to develop, test, and implement an analytical pipeline to be built into the NICSM’s analytical platform.
The project will be entirely computational. Thus, we are seeking a student having a strong interest in computational approaches, evidenced by programming skills (such as in Linux/Shell, Python, and/or R), and preferably with knowledge in biomedical sciences, computational biology and/or statistics. However, students from a 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 PhD project, please contact the PhD supervisors: Dr William Duddy (email@example.com) and/or Dr Priyank Shukla (firstname.lastname@example.org).
- Upper Second Class Honours (2:1) Degree from a UK institution (or overseas award deemed equivalent via UK NARIC)
- 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
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.
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.
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