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; http://panprostate.org) 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: https://www.uea.ac.uk/medicine/people/profile/d-brewer
Acceptable first degree: Computer Science, Physics, Mathematics, Engineering, Biological Sciences, Biochemistry, Biomedical Science. The minimum entry requirement is 2:1 (or equivalent).
Closing date: 31st May 2019 Start date of project: October 2019
Further particulars and an application form can be obtained from: https://t.co/IBEtx9Kojg