Zegami a data visualisation expert support scientists to better understand genetic disease
Since spinning out of the university five years ago, Zegami is supporting scientists in the MRC WIMM’s Centre for Computational Biology to speed up the process of training machine learning tools to find the locations of important proteins that bind to the human genome that turns genes on and off.
When genes are incorrectly activated or deactivated they can cause disease, so accurately finding their control mechanisms is vital.
Steve Taylor, Chief Scientific Officer at Zegami and co-leader of the Centre of Computational Biology at the MRC Weatherall Institute of Molecular Medicine, said: “We are delighted to be working with the MRC WIMM Centre for Computational Biology and supporting them in their incredibly important work.
“The era of big data is delivering vast amounts of information for health practitioners, patients, researchers, and policymakers and data visualization has a huge role to play in terms of generating insight and creating actionable, on-demand knowledge for decision-makers.”
The MRC Weatherall Institute of Molecular Medicine at the University of Oxford was founded in 1989 to foster research in molecular and cell biology, with the aim of improving human health. Through its excellent basic and applied research, it has become a leading centre for translational medicine. Its research has resulted in improved understanding, diagnosis and treatment of a wide range of human diseases.
Jim Hughes, professor of gene regulation at the MRC Weatherall Institute of Molecular Medicine at Oxford University said: “Our ability to sequence and reconstruct entire genomes has transformed our approach to research and medicine. We can now investigate, on the scale of the whole genome, how and in what situations parts of that blueprint are used. This is enhancing our understanding of how our genome works in health and disease, but it also means we are generating huge amounts of data.
“This is an exciting opportunity for us, but also a problem in how best to ensure the data is clean and accurate so that we can train machine learning method effectively and efficiently to create new insights.
“Zegami allows us to visualise, sort filter and label vast amounts of biological data for use in training machine learning models, solving a key challenge in this field. Zegami also allows us to easily publish all the data for scientists to understand how the models were created to help address the machine learning black box problem.”
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