Multimodal Transfer Learning for Aging Clock Development and Disease Target Discovery

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Aging (Aging-US)

Aging-US published this research paper on June 13, 2023, in 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" by researchers from Insilico Medicine, Pak Shek Kok, New Territories, Hong Kong; Insilico Medicine, Masdar City, United Arab Emirates; Insilico Medicine, Shanghai, China. DOI - https://doi.org/10.18632/aging.204788 Corresponding author - Alex Zhavoronkov - alex@insilico.com Abstract 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, we propose a novel approach to multimodal aging clock we 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. 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, we provide a list of promising targets annotated using the PandaOmics industrial target discovery platform. Sign up for free Altmetric alerts about this article - https://aging.altmetric.com/details/email_updates?id=10.18632%2Faging.204788 Subscribe for free publication alerts from Aging - https://www.aging-us.com/subscribe-to-toc-alerts Keywords - aging, transformers, deep learning, therapeutic target discovery, aging biomarkers, human aging About Aging-US Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways. Please visit our website at https://www.Aging-US.com​​ and connect with us: SoundCloud - https://soundcloud.com/Aging-Us Facebook - https://www.facebook.com/AgingUS/ Twitter - https://twitter.com/AgingJrnl Instagram - https://www.instagram.com/agingjrnl/ YouTube - https://www.youtube.com/@AgingJournal LinkedIn - https://www.linkedin.com/company/aging/ Pinterest - https://www.pinterest.com/AgingUS/ Media Contact 18009220957 MEDIA@IMPACTJOURNALS.COM