University of Edinburgh Featured PhD Programmes
University of Oxford Featured PhD Programmes
University of Edinburgh Featured PhD Programmes
University College London Featured PhD Programmes
University of Leeds Featured PhD Programmes

A PhD applying machine learning techniques to Next Generation Sequencing Big Data to improve Prostate Cancer patient outcome. (BrewerD-CooperCU19SF)

Project Description

Prostate cancer (PCa) is the second most common cancer in men worldwide and an estimated 307,000 men annually die from PCa worldwide. The progression of PCa is highly variable, with some cancers laying dormant for many years while others advance rapidly. Risk assessment at the time of diagnosis is a critical step in disease management, determining whether the cancer is simply monitored or there is radical intervention by prostatectomy or radiotherapy. Unfortunately, there is currently no completely reliable approach to predict which tumours will progress and kill the patient.

The last decade has seen an explosion in the amount of global in silico data, which has led to new tools and techniques being developed to optimally utilise it. In medical research, the amount of data available has rapidly increased with the introduction of next generation sequencing. The international Pan-Prostate Cancer Group (PPCG; has produced an unprecedented set of data from over 2000 men with PCa. This consists of whole genome sequencing, methylation, transcriptome, clinical and histopathology information.

By applying cutting-edge machine learning techniques to the multiple layers of clinical and molecular data available from the PPCG you will help build an improved predictor of aggressive disease, reveal novel subtypes of PCa, and gain a greater understanding of PCa aetiology.

This is a bioinformatics/data analysis-based PhD. During the PhD you will gain knowledge on how to deal with “Big Data”, high performance computing, developing pipelines and statistical analyses. You will be part of the Cancer Genetics team at the Norwich Medical School, which is an interdisciplinary team comprising a mixture of bioinformaticians and lab-based scientists. We have a broad interest in translational cancer based molecular studies with the aim of improving patient care.

Research includes urine-based biomarker development, whole genome sequencing studies, cancer-subtype detection and bacteria in cancer studies.

For more information on the supervisor for this project, please go here:

This is a PhD programme.

The start date of this project is 1 April 2020, 1 July 2020 or 1 October 2020.

The mode of study is full-time. The studentship length is 4 years (3 years with 1 year for registration).

Entry requirements:

Acceptable first degree in Computer Science, Physics, Mathematics, Engineering, Biological Sciences, Biochemistry, Biomedical Science.

The standard minimum entry requirement is 2:1 or Master’s.

Please note: Applications are processed as soon as they are received and the project may be filled before the closing date, so early application is encouraged.

Funding Notes

This PhD project is offered on a self-funding basis. It is open to applicants with funding or those applying to funding sources. Details of tuition fees can be found at View Website.

A bench fee may also payable on top of the tuition fee to cover specialist equipment or laboratory costs required for the research. The amount charged annually will vary considerably depending on the nature of the project and applicants should contact the primary supervisor for further information about the fee associated with the project.


1. Wedge, D. C. et al. Sequencing of prostate cancers identifies new cancer genes, routes of progression and drug targets. Nat. Genet. 50, 682–692 (2018).

2. Luca, B. et al. DESNT: A Poor Prognosis Category of Human Prostate Cancer. Eur. Urol. Focus 4, 842–850 (2018).

3. Lalonde, E. et al. Tumour genomic and microenvironmental heterogeneity for integrated prediction of 5-year biochemical recurrence of prostate cancer: a retrospective cohort study. Lancet Oncol 15, 1521–1532 (2014).

4. Gerhauser, C. et al. Molecular evolution of early onset prostate cancer identifies novel molecular risk markers and clinical trajectories. Cancer Cell 996–1011 (2018). doi:10.1016/j.ccell.2018.10.016

5. Gundem, G. et al. The evolutionary history of lethal metastatic prostate cancer. Nature 520, 353–357 (2015).

Email Now

Insert previous message below for editing? 
You haven’t included a message. Providing a specific message means universities will take your enquiry more seriously and helps them provide the information you need.
Why not add a message here
* required field
Send a copy to me for my own records.

Your enquiry has been emailed successfully

FindAPhD. Copyright 2005-2019
All rights reserved.