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Can we predict cancer cell migration and spread using deep and machine learning of pathology images?

  • Full or part time
    Prof R H Insall
  • Application Deadline
    Friday, January 11, 2019
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

About This PhD Project

Project Description

The Cancer Research UK Beatson Institute in Glasgow is one of the world leading centres for cancer research. The Institute provides an outstanding research environment, underpinned by state-of-the-art core services and advanced technologies with special emphasis on imaging, metabolomics and in vivo models of cancer.

This project will use artificial intelligence algorithms – deep learning and machine learning, as appropriate – to understand how the patterns of cells at the edges of tumours allows us to understand what is driving the tumour’s spread, and if it correlates with patient outcomes and the seriousness of the disease. In particular, we are interested in what these patterns of cells can tell us about how cells are communicating with one another, with the tissues surrounding the tumour, and with the immune system.

An appropriate student will have a background including mathematics and/or computation and a good understanding and experience of deep and machine learning, coupled with a desire to understand the biological and medical consequences of cancer; biomedical training will be fully provided.

To apply, please click on the ’Apply Online’ button, which will take you to the Beatson Institute website where you should fill in the application form. Please do not email your CV.

References

1. WASP family proteins and formins compete in pseudopod- and bleb-based migration. Davidson AJ, Amato C, Thomason PA, Insall RH. J Cell Biol. 2018 Feb 5;217(2):701-714. doi: 10.1083/jcb.201705160.

2. Fam49/CYRI interacts with Rac1 and locally suppresses protrusions. Fort L, Batista JM, Thomason PA, Spence HJ, Whitelaw JA, Tweedy L, Greaves J, Martin KJ, Anderson KI, Brown P, Lilla S, Neilson MP, Tafelmeyer P, Zanivan S, Ismail S, Bryant DM, Tomkinson NCO, Chamberlain LH, Mastick GS, Insall RH, Machesky LM. Nat Cell Biol. 2018 Oct;20(10):1159-1171. doi: 10.1038/s41556-018-0198-9. Epub 2018 Sep 24.

3. A G-protein-coupled chemoattractant receptor recognizes lipopolysaccharide for bacterial phagocytosis. Pan M, Neilson MP, Grunfeld AM, Cruz P, Wen X, Insall RH, Jin T. PLoS Biol. 2018 May 25;16(5):e2005754. doi: 10.1371/journal.pbio.2005754.

4. Statistical inference of the mechanisms driving collective cell movement. Ferguson, E. A., Matthiopoulos, J. , Insall, R. H. and Husmeier, D. (2017) J. R. Stat. Soc. C, 66: 869-890. doi:10.1111/rssc.12203

5. LPP3 mediates self-generation of chemotactic LPA gradients by melanoma cells. Susanto O, Koh YWH, Morrice N, Tumanov S, Thomason PA, Nielson M, Tweedy L, Muinonen-Martin AJ, Kamphorst JJ, Mackay GM, Insall RH. J Cell Sci. 2017 Oct 15;130(20):3455-3466. doi: 10.1242/jcs.207514.

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