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Episode Info: In this twentieth episode, Polina Mamoshina introduces recently launched Deep Longevity, and its app (young.ai). Read the transcript Biomarkers of aging are introduced. She explains that they have taken a superior approach by using deep learning instead of machine learning. Aging clocks in general are covered. Finally, she shares her view that transcriptomic and proteonomic clocks are the likely future. Topics we discussed in this episode Personal background: Moscow State University, Oxford University, Insilico Medicine hackathon Bringing Deep Longevity out of stealth, Young.ai companion app Deep Longevity introduction including company aims Description of Young.AI app Biomarkers of aging as the accelerant of market for aging interventions Introduction to aging clocks: Horvath, Hannum Taking a novel and superior technological approach to aging clocks, using deep neural networks, instead of shallow machine learning Limitations of shallow machine learning models Ability of neural networks to capture highly non linear dependencies and what that matters for biological age determination Investing in anticipated payoff from deep learning over the long-term, even if machine learning may be good enough in many cases now Biological age prediction with Aging.ai Two approaches to designing aging clocks Machine learned PhenoAge biological age score Introducing mortality, with the GrimAge score Longevity clinics and life insurance as market Biological age scoring as onboarding tool for life insurance markets Training datasets Common blood analytes used in PhenoAge vs Aging.ai Optimized blood analyte levels for a given individual to get younger Orthodox medicine uses blood analyte levels that are not specific to the individual and not optimized ranges; designed to detect only late-stage pathologies Cheapness of regular blood analytes Emerging market is likely to age score bodily subsystems rather than provide an overall singular biological age score Goal is to find the fastest ticking clock in your body Biological age test using a selfie Providing a library of biological age scores, from free to expensive, so users can upgrade, find out more about themselves Belief that proteomic and transcriptomic clocks will outperform epigenetic clocks in terms of being actionable with interventions Epigenetics and aging Acceleration of the aging rate may show up “late” in terms of being able to intervene, on the epigenome Youthful blood plasma exchanges and age quantification Transcriptomic, proteomic, and glycomic clocks Anticipated rise of longevity clinics Show links Deep Longevity (Company Website) Insilico Medicine (Company Website) Human Longevity, Inc. (Company Website) Regent Pacific Group (Company Website) Young.AI (App from Deep Longevity) Aging.AI (Biological Age Prediction) ‘DNA Methylation Age of Human Tissues and Cell Types’ (Paper) ‘Assessment of Epigenetic Clocks as Biomarkers of Aging in Basic and Population Research’ (Paper) Steve Horvath (Wik...
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