Aging clocks, predicting your real biological age with artificial intelligence:
Dr. Mamoshina is a Chief Scientist at Deep Longevity, Ltd, a Hong Kong-based bioinformatics and deep learning company focused on multiple deep biomarkers of aging dubbed “deep aging clocks” to provide a universal multifactorial measure of human biological age. Dr. Mamoshina graduated from the Department of Genetics of the Moscow State University and received her PhD from the University of Oxford. She was one of the winners of GeneHack a Russian nationwide 48-hour hackathon on bioinformatics at the Moscow Institute of Physics and Technology attended by hundreds of young bioinformaticians. Dr. Mamoshina is involved in multiple deep learning projects at the Pharmaceutical Artificial Intelligence division of Insilico Medicine working on the drug discovery engine and developing biochemistry, transcriptome, and cell-free nucleic acid-based biomarkers of ageing and disease.
EPISODE LINKS:
Deep Longevity: deeplongevity.com
Young.ai: young.ai
Longevity medicine course: longevity-medicine.org
Linkedin: www.linkedin.com/in/polymamoshina
Nanopore: nanoporetech.com/
INFO:
Podcast website: https://volandino.com
Spotify: https://open.spotify.com/show/3O74ctu6Hv5zZdHYT9Ox3Z
Apple Podcasts: https://podcasts.apple.com/us/podcast/beyond/id1509949724
RSS: https://volandino.com/feed/podcast
Full episodes playlist: https://www.youtube.com/playlist?list=PLXRmG-SrXaHiysqmzKg78kW1g_VBxHyjj
OUTLINE:
01:31 – Motivations to work in aging research
03:37 – What is an aging clock? Chronological age vs biological age
05:15 – Biomarkers, Time frames, efficiency, success and failure rates
11:06 – Monitoring our aging, frequency
13:00 – Devices, sensors, Young.ai, Fitbit, Apple health
14:21 – If we are aging faster than we should, how reversible is the situation?
17:26 – Anti aging supplements and medicines – Metformin and others – status
21:30 – Senolytics – killing senescent cells
22:23 – Hope and challenges
23:30 – Intermittent fasting
27:30 – Examples of aging clocks – Epigenetic clocks – slow change
30:43 – More responsive & cheaper aging clocks – blood biochemistry
31:52 – Applying A.I and deep learning to building aging clocks
35:00 – The role of deep learning and A.I in longevity research
36:00 – The challenges of using A.I: training, hardware, talent, etc
37:32 – Working on companies that have a positive impact in society
39:13 – How we think about aging – past, present and future
41:00 – Transcriptomic & Proteomic aging clocks
43:05 – RNA related metrics
45:18 – Protein related metrics
46:27 – Compact devices that analyze biomarkers – example: nanoporetech
48:09 – The challenges of working with RNA
49:00 – In our hands we have natural protections against RNA
50:10 – The bright future of aging clocks
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