PrecisionLife Project Awarded Innovate UK Grant to Improve Diagnosis and Treatment of ME/CFS and Long Covid

About PrecisionLife

PrecisionLife® is a pioneering computational biology company changing the way the world predicts, prevents, and treats chronic disease by finding better, more personalized treatment options for patients.

With a vast repository of intellectual property and unique insights into disease biology across over 50 indications, we have developed the most extensive drug discovery pipeline of precision targeted therapies.

Our innovative precision medicine analytics approach enables us to discover new subgroups of patients with mechanism-based patient stratification biomarkers to understand the causes of complex diseases, identify new treatments, derisk and accelerate clinical trials, develop accurate diagnostic tools, and personalize risk prediction and disease prevention for people with unmet medical needs.

To learn more about our groundbreaking work, please visit precisionlife.com, connect with us on LinkedIn at @PrecisionLifeAI and follow us on X/Twitter at @PrecisionLifeAI.

About Action for M.E.

Action for M.E. is a dynamic charity committed to improving the lives of individuals impacted by Myalgic Encephalomyelitis (M.E.). The organisation focuses on support, advocacy, and research, striving to bring positive change for those living with M.E. Their mission includes raising awareness, driving research initiatives, providing high-quality healthcare and information services, and establishing crucial peer networks. Through collaboration and understanding, Action for M.E. aims to improve the quality of life for individuals navigating the challenges of this debilitating disease. For more information or to access support, visit our websiteYou can also follow us on InstagramX\Twitter and Facebook and connect with us on LinkedIn. 

About the MRC Human Genetics Unit, University of Edinburgh

The mission of the MRC Human Genetics Unit is to understand how the human genome works and how changes in DNA affect health and cause disease.

Genome interpretation is critical for the diagnosis of genetic diseases and cancers and for the development of new and better-tailored treatments for people living with these conditions.

The Unit combines the latest computational and experimental technologies while bringing together researchers and clinicians from various disciplines to deliver on this mission.

About DecodeME

The DecodeME DNA study is the largest genetic study in the world, which aims to build understanding about myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and ultimately find treatments. 

The study is led by the DecodeME Partnership, which brings together a unique collaboration of researchers, people with ME/CFS, carers and the public to advance understanding about the disease. 

A team of genetics experts at the MRC Human Genetics Unit, University of Edinburgh, led by Professor Chris Ponting, will run the science arm of the study. They are working in equal partnership with the charity Action for ME, the Forward ME alliance of UK charities, Medical Research Council (MRC), the National Institute for Health and Care Research (NIHR) and people with lived experience of ME/CFS. The robust scientific study is backed by all the main ME/CFS charities in the UK.

About Innovate UK

Innovate UK is creating a better future by inspiring, involving and investing in businesses developing life-changing innovations.

We provide targeted sectors with expertise, facilities, and funding to test, demonstrate and evolve their ideas, driving UK productivity and economic growth. Join our network and communities of innovators to realise the potential of your ideas and accelerate business growth.

References

¹Das, S., Taylor, K., Kozubek, J. et al. Genetic risk factors for ME/CFS identified using combinatorial analysis. J Transl Med 20, 598 (2022). https://doi.org/10.1186/s12967-022-03815-8

²Taylor, K., Pearson, M., Das, S. et al. Genetic risk factors for severe and fatigue dominant long COVID and commonalities with ME/CFS identified by combinatorial analysis. J Transl Med 21, 775 (2023). https://doi.org/10.1186/s12967-023-04588-4