Collaborating Authors

Deep Aging Clocks: The emergence of AI-based biomarkers of aging and longevity


Summary: Combining multiple artificial intelligence agents sheds light on the aging process and can help further understanding of what contributes to healthy aging. There are two kinds of age: chronological age, which is the number of years one has lived, and biological age, which is influenced by our genes, lifestyle, behavior, the environment, and other factors. Biological age is the superior measure of true age and is the most biologically relevant feature, as it closely correlates with mortality and health status. The search for reliable predictors of biological age has been ongoing for several decades, and until recently, largely without success. Since 2016 the use of deep learning techniques to find predictors of chronological and biological age has been gaining popularity in the aging research community.

Expert: Human Immortality Could Be Acquired Through AI


More and more scientists are convinced that aging, while a natural phenomenon experienced by all living creatures, is a disease that can be treated or even cured. Scientists, generally, have taken different approaches to aging in that regard. Some want to slow down the process, while others seek to put a stop to it altogether. Those in the latter group see no limit in our potential to extend human life. These efforts are fueled by the latest technologies science has to offer.

Machine learning to assist in building muscle


IMAGE: Insilico Medicine developed a novel deep-learning based model that predicts a biological age of a muscle. Thursday, July 5th, Rockville, MD - Insilico Medicine, a Rockville-based next-generation artificial intelligence company specializing in the application of deep learning for target identification, drug discovery and aging research announces the publication of a new research paper "Machine learning on human muscle transcriptomic data for biomarker discovery and tissue-specific drug target identification" in Frontiers in Genetics journal. Sarcopenia (from Greek "flesh poverty"), is one of the major age-related processes and involves the loss of skeletal muscle and its function. Age-associated muscle wasting remains an important clinical challenge that impacts hundreds of millions of older adults. It is associated with serious negative health outcomes such as falls, impaired standing balance, physical disability, and mortality.

Artificial intelligence tracks biological age at every level and rewinds the aging clock


Monday, December 3, Rockville, MD - Insilico Medicine, one of the leaders in artificial intelligence for drug discovery, biomarker development, digital medicine, and aging research announced today the publication of its recent paper titled "Artificial Intelligence for Aging and Longevity Research: Recent Advances and Perspectives" in Ageing Research Reviews, one of the highest-impact journals in the field. The paper introduces recent advances in deep learning for aging research and provides fair insight into the field. The emergence of the longevity biotechnology industry has brought many biotech and pharma companies and academic research institutions to the longevity landscape, and now one of the key trends accelerating the field is recent advances in artificial intelligence. "Insilico Medicine is dedicated to extending human longevity. We came up with several very important realizations. First, age is one of the most abundant biological features, and when your data looks like Swiss cheese, age is present. Second, Deep Learning (DL) age predictors are a great way to integrate previously incompatible data types, such as videos and blood test results. Third, the generation of new biological data using Generative Adversarial Networks (GANs), with age as a generation condition, is a great way to produce high-quality synthetic data. Also, it is possible to view aging as a staged disease to get a holistic view of the biological process on both tissue-specific and systemic levels, which makes the Deep Neural Networks (DNNs) more interpretable, builds causal graphs, and identifies biological targets. Moreover, it is possible to train the DNNs on age and retrain the model on specific diseases. Also, it is possible to use biological aging clocks to personalize immunotherapies and vaccinations and to identify new ways to improve response rates. The paper outlines these realizations and presents a way to accelerate aging research using AI technologies", said Alex Zhavoronkov, Ph.D., founder and CEO of Insilico Medicine, who led the study.

MouseAge.Org: Artificial intelligence for photographic biomarkers in mice


IMAGE: MouseAge.Org provides tools for cross-species analysis, and provide correlations between health and appearance. Tuesday, 29th of August, 2017, Baltimore, MD - Insilico Medicine, Inc, a Baltimore-based next-generation artificial intelligence company, today announced its participation in the The scientists from Insilico Medicine will collaborate with scientists from Harvard, Oxford, Youth Laboratories, the Biogerontology Research Foundation, and other institutions to enable scientists worldwide to derive more information from rodent studies, develop novel biomarkers of aging and various diseases in mice, develop tools for cross-species analysis, and provide correlations between health and appearance. The project campaign has been launched today at research crowdfunding platform The project was conceived by Vadim Gladyshev, Professor of Medicine at Brigham and Women's Hospital, Harvard Medical School, and Alex Zhavoronkov, CEO of Insilico Medicine.