Cancer treatment could become more effective thanks to new algorithms which can predict millions of gene interactions

phys.org | 9/3/2014 | Staff
Celtics2212 (Posted by) Level 3
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While network algorithms are usually associated with finding friends on social media, researchers at the University of Sussex have shown how they could also be used improve the effectiveness of cancer treatment, by predicting the interactions between genes.

There are over 12 million newly diagnosed cases of cancer globally each year and this figure only continues to grow.

Treatments - Chemotherapy - Agents - Effectiveness - Side-effects

Existing treatments like chemotherapy involve non-selective agents that have limited effectiveness and strong side-effects. As a result, scientists believe there is a desperate need for improved treatments which are more personalised and more targeted towards cancerous cells.

There are a number of targeted cancer therapies already being developed that exploit a gene relationship called 'synthetic lethal interactions'. The trouble is, up until now, relatively few of these interactions have been identified.

Thanks - Use - Intelligence - Researchers - University

Thanks to the use of artificial intelligence, researchers at the University of Sussex, working with a team from the Institute of Cancer Research in London, have successfully created an algorithm which can now predict where these interactions may occur.

Graeme Benstead-Hume, a doctoral student at the University of Sussex, said: "Synthetically lethal means that cells can cope if either one of its proteins does not work, but will die if neither of the proteins is functioning.

Relationships - Drug - Treatments - Cancer - Cells

"These relationships are important because they can be used to identify where potential drug treatments could target just the cancer cells yet leave healthy cells unharmed, creating a more effective, gentler treatment.

"With breast cancer,...
(Excerpt) Read more at: phys.org
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