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Job: UK/EU funded PhD Studentship: Detecting novel subtypes of cancer using data science and machine learning
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gravatar for Daniel Brewer
10 months ago by
United Kingdom
Daniel Brewer70 wrote:

Further details here: https://goo.gl/hsaNuM

Cancer is not a single disease but a collection of diseases arising in a wide range of tissues. Even within a single cancer type such as breast cancer there can be multiple forms. Identifying subtypes will aid individualised treatment for improved survival and benefit the lives of those affected by cancer. Cancer subtypes can be identified by analysing what genes are switched on/upregulated or are turned off/downregulated. Unfortunately, analysis of these data is complex, and has been largely unsuccessful, with the notable exception of breast cancer. 

We have previously had success in applying complex clustering methods to data from prostate cancer samples, identifying a subtype called DESNT associated with poor prognosis. In this studentship, you would apply these methods to a dataset from the Cancer Genome Atlas, a large US project that generated comprehensive high-dimensional data mapping key changes in a large number of different cancers (https://cancergenome.nih.gov). Developing an automated pipeline to analyse large and interrogating these results would form a key part of this PhD, with the expectation of discovering and classifying groundbreaking novel cancer subtypes. 

This is a bioinformatics/data analysis-based PhD. During the PhD you will gain knowledge on how to deal with BigData, 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, subtype detection and bacteria in cancer studies. 

Deadline: 9th April 2018 (UK/EU funded)

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