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Deep Longevity and Longenesis collaborate to clock aging

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Start the clock to stop the clock – centralised data will aid Longevity research. 

Deep Longevity, which engages in the development of explainable AI systems to track the rate of aging at the molecular, cellular, tissue, organ, system, physiological and psychological levels, has entered into a partnership with Longenesis, a company focused on consent-enabled safe data curation for research.

Longevity.Technology: Aging clocks are big news. From skin age to DNA methylation, advances in AI, combined with ever-growing datasets, have led to an explosion in the field. Not only do aging clocks increase the variety of biomarkers that could predict biological age, but they speed identification of novel therapeutic targets. In addition, deep aging clocks can be used for data quality control and evaluation of the biological relevance and value of various data types and combinations. 

This collaboration will integrate Longenesis’ consent management system developed by Longenesis into the Deep Longevity digital platform (which already includes Young.AI, a web-based tracker of aging and wellness). The companies will also be developing a federated learning framework together.

“At Deep Longevity we are working on creating a network of hospitals and clinics that will have access to our aging clocks. To enable this network, we are aiming to create a federated learning pipeline, that will allow us to train multiple new aging clocks without the need to transfer user data,” explains Polina Mamoshina, PhD, Chief Scientific Officer of Deep Longevity, a Regent Pacific company.

Longenesis graphic showing data sharing. Source: Longenesis

“Longenesis is a company invested by LongeVC, a venture fund, and accelerator dedicated to growing the longevity ecosystem in the European Union,” says Garry Zmudze, founding partner of LongeVC, an investor in both Longenesis and Deep Longevity. “Over the past few years, Longenesis developed a range of technologies to help protect user privacy and manage consent to help companies provide the individuals with more tools to take control over their data.”

 


 

 

“At Longenesis we believe that the need for centralized, compliant and seamless biomedical data asset identification is crucial for collaborative research initiation, faster patient recruitment and timely response to global healthcare challenges,”

 


 

“At Longenesis we believe that the need for centralized, compliant and seamless biomedical data asset identification is crucial for collaborative research initiation, faster patient recruitment and timely response to global healthcare challenges,” comments Sergejs Jakimovs, a CEO of Longenesis. “We are looking forward to this collaboration, creating a federated learning pipeline and embracing the “data stays local” principle at the same time.”

As the number of clocks and data extrapolated from them grows, having ready access available to hospitals, clinicians and researchers could prove invaluable in the fight against aging, as well as helping to combat age-related diseases. At Longevity.Technology we feel there is a need for a common standard for data management of client/patient data in the Longevity field. It’s a win/win as easier onboarding (one form for multiple biomarkers, one portal to manage results and plan interventions, &c), means more people are likely to start using aging clock technology themselves.

Image credit: By helloabc / Shutterstock

1 COMMENT

  1. […] Deep Longevity has entered into a partnership with Longenesis; this collaboration will integrate Longenesis’ consent management system into the Deep Longevity digital platform (which already includes Young.AI, a web-based tracker of ageing and wellness). The companies will also be developing a federated learning framework together.  […]

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Eleanor Garth
Deputy Editor Now a science and medicine journalist, Eleanor worked as a consultant for university spin-out companies and provided research support at Imperial College London and various London hospitals in a former life.
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