A multimodal deep learning model to predict individual cancer patient survival probabilities

Dr Peter Hall, Dr Colin McLean, Prof Joanne Edwards

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Labs:
Hall Lab
Edwards Lab
Computational Biology
Duration: 4 years from September 2022
Closing Date: 27th May 2022

About us

Applications are invited from outstanding candidates to join a Cancer Research UK funded PhD programme at the Edinburgh Cancer Research Centre. These are funded by the Cancer Research UK Scotland Centre, a joint initiative between Edinburgh and Glasgow which brings together cancer scientists and clinicians from across the Universities of Edinburgh and Glasgow, delivering outstanding cancer research and improved patient care. The Cancer Research UK PhD programme is integrated into the research activities of the Centre with over 80 principal investigators contributing to this cross-disciplinary programme spanning from fundamental science to translational research. Research projects benefit from state-of-the art facilities for genomics, mass spectrometry, advanced microscopy, single cell technologies, and from advanced computational and informatics capabilities.

Project Description

Can a deep learning framework that accommodates complex cancer 'omics data, such as gene-expression data, with clinical phenotypic data enhance our ability to predict cancer patient survival?

Deep neural networks are versatile machine learning techniques, which make it possible to build frameworks which learn from multi-modal data that can outperform traditional modelling methods. Such methods require large amounts of data, which has limited their use to date. New data opportunities in Scotland will now enable us to enhance research patient cohorts containing detailed 'omics data with rich NHS clinical data on patient characteristics, cancer characteristics, treatment information and a range of clinical outcomes. Combining real-world clinical data with existing research data will allow us to address this question, initially with the priority cancer types of colorectal cancer, mesothelioma and ovarian cancer.

The project will use software packages such as TensorFlow and Keras (R, Python). Methods will be explored for handling dimensional reduction of genomics, for example, by using unsupervised techniques, building clustered gene-expression networks, the use of gene and pathway layers in the neural network model.

Improving our ability to predict disease-specific survival and other relevant patient outcomes using the full breadth of data is important, not only for the discovery and development of novel biomarkers, but also as a tool to help guide clinicians and patients in their choice of treatments and to better understand their prognosis.

This cancer informatics project will lever clinical and biological informatics expertise in Edinburgh and Glasgow to develop and implement the use of deep learning applied to the full breadth of pertinent data.

Application Procedure

Up to 3 studentships are available to start in September 2022 for outstanding applicants with a stipend of £19,000 p/a. These are funded by the CRUK Scotland Centre, a joint initiative between Edinburgh and Glasgow. Successful students will be registered for their degree in either Glasgow or Edinburgh, depending on the project they apply for.

We are looking for students with a very good degree in a Life Sciences subject and an aptitude for experimental work, who are also highly committed to pursuing a PhD and a career in cancer research. You should hold at least an upper second-class degree in a relevant subject and comply with English language requirements.

All applications will be administered centrally via the University of Edinburgh, please apply on the link below- this includes Glasgow-based projects with Glasgow-based supervisors: https://www.star.euclid.ed.ac.uk/public/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=PRPHDCECRC1F&code2=0020

Closing date: 27 May 2022

Interviews are expected to be held week beginning 27 June.

Applications are open to all individuals irrespective of nationality or country of residence.

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