Tag: Genetics
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BCI researcher receives UKRI Future Leaders Fellowship Award
9th September 2021
Dr Benjamin Werner from Barts Cancer Institute, Queen Mary University of London, is one of the next generation of UK science leaders to receive funding through UK Research and Innovation’s (UKRI) Future Leaders Fellowships scheme. Dr Werner will receive an award of approximately £1.4 million, which will support a research project looking at the evolutionary dynamics of circular extra-chromosomal DNA (ecDNA) in human cancers.
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Dissecting complex biological pathways with machine learning
19th July 2021
We spoke with Group Leader Dr Jun Wang and Postdoctoral Researcher Dr Anthony Anene from Barts Cancer Institute’s Centre for Cancer Genomics & Computational Biology about their most recent publication. Published in Patterns, the paper describes the development of a machine-learning tool called ACSNI that can be used to predict tissue-specific pathway components from large biological datasets.
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BCI researcher part of team shortlisted for Cancer Grand Challenges awards
23rd June 2021
Dr Benjamin Werner from Barts Cancer Institute, Queen Mary University of London, is part of an international team that has been selected to share its ideas on how to solve one of cancer’s toughest challenges.
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SNPnexus in the fight against COVID-19
8th February 2021
Researchers from Barts Cancer Institute, Queen Mary University of London, have released a web-based tool called SNPnexus COVID to streamline the analysis of host genetic sequencing data and allow for the identification and prioritisation of genetic variants that may impact an individual’s susceptibility to and the severity of COVID-19 infection.
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Research explainer with Dr Gabriella Ficz
28th September 2020
We spoke with Dr Gabriela Ficz to find out about her recent publication in Nature Communications. The study set out to determine how embryonic stem cells can acquire features that are found in cancer cells.
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Immune ‘cloaking’ in cancer cells
14th September 2020
Researchers have created a mathematical model that can determine the impact of the immune system on tumour evolution. The information gained from using this model may be able to be used to predict whether immunotherapy is likely to be effective for a patient’s cancer, helping to guide treatment decisions.
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