Precious1GPT: Multimodal Transfer Learning for Aging Clock Development and Target Discovery
06-20-2023“The development of Precious1GPT [...] has demonstrated the potential of our approach in deciphering the molecular mechanisms of aging.”
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BUFFALO, NY- June 20, 2023 – A new research paper was published in Aging (listed by MEDLINE/PubMed as "Aging (Albany NY)" and "Aging-US" by Web of Science) Volume 15, Issue 11, entitled, “Precious1GPT: multimodal transformer-based transfer learning for aging clock development and feature importance analysis for aging and age-related disease target discovery.”
Aging is a complex and multifactorial process that increases the risk of various age-related diseases and there are many aging clocks that can accurately predict chronological age, mortality, and health status. These clocks are disconnected and are rarely fit for therapeutic target discovery.
In this study, researchers Anatoly Urban, Denis Sidorenko, Diana Zagirova, Ekaterina Kozlova, Aleksandr Kalashnikov, Stefan Pushkov, Vladimir Naumov, Viktoria Sarkisova, Geoffrey Ho Duen Leung, Hoi Wing Leung, Frank W. Pun, Ivan V. Ozerov, Alex Aliper, Feng Ren, and Alex Zhavoronkov from Insilico Medicine propose a novel approach to multimodal aging clock, which they call Precious1GPT, utilizing methylation and transcriptomic data for interpretable age prediction and target discovery developed using a transformer-based model and transfer learning for case-control classification.
“To identify aging biomarkers associated with age-related diseases, in the present work, we combined the ability of aging clocks to predict biological age and thus grasp molecular changes accompanied by senescence and our target ID approach to establish genes that are related to the development of diseases.”
While the accuracy of the multimodal transformer is lower within each individual data type, compared to the state of art specialized aging clocks based on methylation or transcriptomic data separately, it may have higher practical utility for target discovery. This method provides the ability to discover novel therapeutic targets that hypothetically may be able to reverse or accelerate biological age providing a pathway for therapeutic drug discovery and validation using the aging clock. In addition, the researchers provided a list of promising targets annotated using the PandaOmics industrial target discovery platform.
“The transformer-based model allowed for the integration of multi-omics data and improved the accuracy of the aging clock, while the transfer learning approach facilitated the identification of disease-related genes in the context of aging.”
Read the full study: DOI: https://doi.org/10.18632/aging.204788
Corresponding Author: Alex Zhavoronkov
Corresponding Email: alex@insilico.com
Keywords: transformers, deep learning, therapeutic target discovery, aging biomarkers, human aging
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About Aging-US:
Aging publishes research papers in all fields of aging research including but not limited, aging from yeast to mammals, cellular senescence, age-related diseases such as cancer and Alzheimer’s diseases and their prevention and treatment, anti-aging strategies and drug development and especially the role of signal transduction pathways such as mTOR in aging and potential approaches to modulate these signaling pathways to extend lifespan. The journal aims to promote treatment of age-related diseases by slowing down aging, validation of anti-aging drugs by treating age-related diseases, prevention of cancer by inhibiting aging. Cancer and COVID-19 are age-related diseases.
Aging is indexed by PubMed/Medline (abbreviated as “Aging (Albany NY)”), PubMed Central, Web of Science: Science Citation Index Expanded (abbreviated as “Aging‐US” and listed in the Cell Biology and Geriatrics & Gerontology categories), Scopus (abbreviated as “Aging” and listed in the Cell Biology and Aging categories), Biological Abstracts, BIOSIS Previews, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science).
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