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Algorithmic Investigation of Large Biological Data sets

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If you have a question about this talk, please contact Dr Vivien Gruar.

Recently the rapid growth of both internal and external data sources in conjunction with large external databases has increased the need for GSK to address the most complex problems in drug discovery. For example, the chemical database ChEMBL, coupled with various biological databases internal and external to GSK with have meant that there is presently an enormous set potential set of research avenues that will yield biologically interesting insights. Such datasets provide a rich environment for deployment of algorithms such as Tensor flow, Deepchem or Topological Data Analysis depending on the form of the data.

In this project, the student will explore and create several algorithms that will be applied to curated datasets to test a range of biological hypothesis. This project is relatively open-ended and so the student should be ready to explore and evaluate current academic work and applicable solutions. The student should be prepared to collaboratively suggest viable hypothesis based on the data at hand.

The student should also be prepared, with aid from supervisors and contacts within the company, to demonstrate their findings in the form of visualizations, code-based models, or another appropriate medium.

This talk is part of the Cambridge Mathematics Placements Seminars series.

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