Tag: Computer models

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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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Using AI to study tumour evolution

2nd September 2020

Researchers have developed a computation model that can reconstruct the evolutionary history of cancer. By unravelling the genetic complexity of a tumour, the tool can be used to better understand how the cancer has developed and may help to guide treatment strategies in the future.

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Evolutionary Insights: From cancer to corals

27th August 2020

Dr Benjamin Werner has teamed up with evolutionary ecologists as part of a new research collaboration, funded by a $1 million research grant from the Human Frontier Science Program.

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Protein network rewiring in cancer

20th January 2020

Research published in Nature Biotechnology has identified new ways to analyse the complexity of the internal workings of normal cells and cancer cells. The study highlights how genetic changes rewire the biochemistry of cancer cells and may aid in identifying new drug targets specific for a patient’s disease.

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‘Chromosomal Catastrophes’ in Colorectal Cancer

5th September 2018

Understanding how cancers develop and change over time is a big challenge. For obvious reasons, scientists can’t simply sit and watch a cancer growing in a person. Members of the Evolution and Cancer Laboratory at the BCI, including lead author Dr William Cross, were part of a collaborative team that set out to identify when particular genetic changes arise during bowel cancer development.

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Forecasting the evolution of cancer

28th May 2018

New research, published today in Nature Genetics, has developed a computer model that forecasts the changes that occur within tumours as they develop. In the future, it is hoped that such a model may enable the prediction of the trajectory of tumour growth in patients, allowing clinicians to pre-empt disease course and tailor treatment regimens accordingly.

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