In what ways can AI be used to improve longevity research and/ or medicine?
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Deep Aging Clocks that estimate an individual's biological age
- Deep Blood Biochemistry and Cell-Count Aging Clocks: The first deep aging clock published by Zhavoronkov in 2016 utilized deep neural networks (DNNs). Using blood tests, the team generated a dataset of over 60,000 healthy subjects. DNNs outperformed the machine learning models in every test. In another study, population differences in the aging clocks were identified by three trained DNNs. As an example, "testing Korean and Eastern European data with a DNN trained on Canadian data revealed that Koreans on average appeared younger than their chronological age".
- Deep Imaging Aging Clocks: A research team at Haut.AI exploited photoimaging. The deep photographic aging clock uses the images of the corners of the eye and can predict the age of an individual within an accuracy of 1.9 years mean absolute error.
- Deep Transcriptomic Aging Clocks: In 2018, the first transcriptomic aging clock developed using deep and other machine learning techniques based on gene expression data from muscle tissue was published. It developed was in which genes could be prioritized as possible targets for pharmaceutical intervention in sarcopenia and other muscle-wasting diseases.
- Other Data Types: Wearable devices and mobile phones provide huge amounts of biologically relevant data. In 2018, age-associated changes in physical activity were studied to predict age using neural networks. A deep learning-based model trained on activity-monitor data predicted age with high accuracy.
Zhavoronkov, A., Bischof, E. & Lee, KF. Artificial intelligence in longevity medicine. Nat Aging 1, 5–7 (2021). https://doi.org/10.1038/s43587-020-00020-4
Zhavoronkov A, Mamoshina P. Deep Aging Clocks: The Emergence of AI-Based Biomarkers of Aging and Longevity. Trends Pharmacol Sci. 2019 Aug;40(8):546-549. doi: 10.1016/j.tips.2019.05.004. Epub 2019 Jul 3. PMID: 31279569.
Deep algorithms for early detection of diseases
Ying-Chen Claire Hou, Hung-Chun Yu, Rick Martin, Elizabeth T. Cirulli, Natalie M. Schenker-Ahmed, Michael Hicks, Isaac V. Cohen, Thomas J. Jönsson, Robyn Heister, Lori Napier, Christine Leon Swisher, Saints Dominguez, Haibao Tang, Weizhong Li, Bradley A. Perkins, Jaime Barea, Christina Rybak, Emily Smith, Keegan Duchicela, Michael Doney, Pamila Brar, Nathaniel Hernandez, Ewen F. Kirkness, Andrew M. Kahn, J. Craig Venter, David S. Karow, C. Thomas Caskey. Precision medicine integrating whole-genome sequencing, comprehensive metabolomics, and advanced imaging. Proceedings of the National Academy of Sciences Feb 2020, 117 (6) 3053-3062; DOI: 10.1073/pnas.1909378117
Machine learning-powered molecular docking tools to predict the side-effects of novel anti-aging therapies
- A Novel Triple Matrix Factorization Method for Detecting Drug-Side Effect Association Based on Kernel Target Alignment
- Prediction of Drug Side Effects with a Refined Negative Sample Selection Strategy
Sukyung Seo, Taekeon Lee, Mi-hyun Kim, Youngmi Yoon, "Prediction of Side Effects Using Comprehensive Similarity Measures", BioMed Research International, vol. 2020, Article ID 1357630, 10 pages, 2020. https://doi.org/10.1155/2020/1357630
Xiaoyi Guo, Wei Zhou, Yan Yu, Yijie Ding, Jijun Tang, Fei Guo, "A Novel Triple Matrix Factorization Method for Detecting Drug-Side Effect Association Based on Kernel Target Alignment", BioMed Research International, vol. 2020, Article ID 4675395, 11 pages, 2020. https://doi.org/10.1155/2020/4675395
Haiyan Liang, Lei Chen, Xian Zhao, Xiaolin Zhang, "Prediction of Drug Side Effects with a Refined Negative Sample Selection Strategy", Computational and Mathematical Methods in Medicine, vol. 2020, Article ID 1573543, 16 pages, 2020. https://doi.org/10.1155/2020/1573543
An integrative multifactorial AI-powered aging metric
Mamoshina, P., Kochetov, K., Putin, E., Cortese, F., Aliper, A., Lee, W.-S., Ahn, S.-M., Uhn, L., Skjodt, N., Kovalchuk, O., et al. (2018). Population Specific Biomarkers of Human Aging: A Big Data Study Using South Korean, Canadian, and Eastern European Patient Populations. Journals Gerontol. Ser. A 73, 1482–1490.