Genomics plc presenting at Sackler Forum

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Genomics plc presenting at Sackler Forum

We are pleased to highlight that our academic founders are presenting at three scientific meetings during the next week, including the prestigious Sackler Forum. Each will be focusing on the potential to utilise genomic and linked healthcare data to build a biological ‘Human Wiring Diagram’, with a discussion of Genomics plc’s progress along this path.

The Raymond and Beverly Sackler U.S.-U.K. Scientific Forum was established to help the scientific leadership of the United Kingdom and the United States forge a partnership on pressing topics of worldwide scientific concern. Organised jointly by the National Academy of Sciences and the Royal Society, this year’s meeting concerns ‘The Future of Machine Learning’, with Professor McVean’s talk, on 1st February, entitled ‘Building the human wiring diagram from linked genomic and healthcare data’.

Professor Donnelly is also presenting on this theme today at the Precision Medicine World Conference, and Dr Chris Spencer will be presenting at next week’s Festival of Genomics.

We also note that Professor Donnelly, who Chairs the Royal Society’s Machine Learning Working Group, participated in the recent of the Royal Society’s Science Matters event in the Royal Festival Hall in London, entitled Machine Learning and Artificial intelligence, which was presented by Brian Cox. The video is available here.

The Human Wiring Diagram
Large-scale analyses of naturally occurring genetic variation in humans have revealed many insights into the genetic and functional architecture of complex disease. However, our ability to combine such data, and so build a causal map linking genetic perturbation to its functional impact on molecules, cells, physiology and health, has been hampered by the challenges of assembling, harmonizing and analysing such large, high dimensional and heterogeneous data sources. Genomics plc has developed high throughput approaches to the acquisition, processing, quality-control and analysis of multiple and heterogeneous data sets spanning over 700 studies and 2.5 million participants. We are using these data to learn the underlying biological structure of pathways leading to disease and subsequent progression and so identify opportunities for the development, validation and repositioning of medical interventions.