Researchers have developed an AI-powered drug discovery tool that validates novel drug candidates in just 46 days, significantly reducing the traditional 2-3 year timeline. The foundation aims to motivate researchers to harness AI's potential in longevity research and encourage larger developers to adopt AI-powered programs.
Biogerontology Research Foundation's Chief Science Officer, Professor Alexander Zhavoronkov, will share his projections on how AI is set to revolutionize biopharma for aging and biomarker discovery. He will reveal the latest progress in AI and drug discovery at Pioneers18 in Vienna.
The international team's roadmap outlines research directions to boost human radioresistance through gene therapy, radioprotective mechanisms, and regenerative technologies. This could extend the healthspan of spacefarers and enable safe long-term space colonization.
The Biogerontology Research Foundation and its partners emphasized the importance of AI in precision medicine at the Precision Medicine World Conference. Two startups, Abreos and Immusoft, emerged as semifinalists for their innovative approaches to precision medicine, including point-of-care assays and immune system programming.
Researchers at AgeX Therapeutics, Insilico Medicine, and the Biogerontology Research Foundation used deep learning techniques to analyze gene expression data in embryonic stem cells. They identified genes, including COX7A1, that contribute to the regenerative capacity of embryos and ESCs, with potential applications in cancer therapies.
Researchers have identified genes implicated in the remarkable regenerative capacity of embryos and ESCs. COX7A1 was found to be dysregulated in various cancer types, suggesting its potential as a novel cancer therapy.
Researchers investigated shRNA therapy for Huntington's Disease, identifying novel methods to modulate construct expression and reduce off-target effects. The study proposes two feedback mechanisms to control shRNA expression, inspired by synthetic biology.
Researchers used AI to analyze over 800 natural compounds for similarities in safety and gene-level similarity to metformin and rapamycin. Novel candidate mimetics of these compounds, such as allantoin and ginsenoside, were identified with potential less adverse effects.
The Biogerontology Research Foundation's Managing Trustee, Dmitry Kaminskiy, will present on the synergistic convergence of AI and Blockchain in healthcare. The conference aims to transform clinical translation and validation of therapies aiming to extend healthy, productive longevity.
BGRF scientists introduce a blockchain-enabled framework to decentralize patient data management, incentivizing patients to contribute to clinical databases. This approach could accelerate progress in biomedicine by increasing data quality and comprehensiveness.
Scientists propose Induced Cell Turnover (ICT) to coordinate endogenous cell ablation with replacement cell administration. This method aims to manually vacate niches for new cells to engraft, minimizing the formation of scar tissue and promoting controlled turnover of aged tissues.
The Biogerontology Research Foundation will present its work on applying artificial intelligence and deep learning to combat aging and age-related disease. The foundation has published seminal papers demonstrating the potential of these approaches to accelerate drug discovery and development, reducing costs and risks.
The Biogerontology Research Foundation is helping to develop artificial intelligence for accelerated drug discovery in aging and age-related diseases. Researchers have made significant progress in using deep learning-based approaches to characterize biomarkers of ageing and predict the chronological age of patients.
The DrugAge database, the largest such database in the world, has been announced by scientists from the Biogerontology Research Foundation and University of Liverpool. The database contains 418 compounds targeting various age-related pathways, revealing that most have yet to be targeted pharmacologically.
The Biogerontology Research Foundation and collaborators announce the development of a novel approach to analyzing transcriptomic data sets, titled iPANDA. The system applies deep learning algorithms to identify patient-specific pathway signatures associated with breast cancer patients.
A biologically younger woman demonstrates the world's first successful gene therapy against human aging. Elizabeth Parrish's white blood cells showed a 20-year increase in telomere length, implying a reversal of age-related diseases.
The Biogerontology Research Foundation proposes classifying aging as a disease to tackle chronic conditions and attract resources to aging research. The proposed classification system would provide a framework for treating aging as a unique, multisystemic disease, alleviating financial, social, and moral burdens.
The Deep Knowledge Life Sciences (DKLS) investment fund will invest in companies with breakthrough technologies, focusing on genetics of age-related diseases, oncology, and regenerative medicine. DKLS will combine AI insights from VITAL with top-level expertise to create analyses and treatments for diseases while de-risking investments.
The Biogerontology Research Foundation has developed an in silico method to predict the effectiveness of cognitive enhancers. The research uses gene expression data to evaluate activated or suppressed signalling pathways in the brain. This approach can help identify potential geroprotectors that also enhance cognitive function.
The Biogerontology Research Foundation will present new economic longevity research at the second Big Data Science in Medicine congress in Oxford. The research, recently published in Psychology Research and Behavior Management, details an extensive survey of International Employee Benefits Association members.
Researchers predict near-future treatments for chronic diseases and aging, targeting underlying biological processes. Innovative business models and flexible regulations are needed to accelerate these advancements.
The OncoFinder algorithm reduces errors in transcriptome analysis by mapping gene expression onto signalling pathways, allowing for more effective evaluation and analysis. The method enables scientists to characterise functional states of transcriptomes more accurately, improving research and clinical applications.