March 13
@
12:00 PM
–
1:00 PM
Understanding Aging at Multi-scale Using Explainable AI
Wei Qiu
PhD Candidate
Computer Science and Engineering Department
University of Washington
Abstract:
As human lifespans continue to extend, it becomes increasingly critical to understand the aging process not only to increase lifespan but also to enhance healthspan. This talk explores how Artificial Intelligence (AI), coupled with Explainable AI (XAI), can illuminate the complex mechanisms of aging at multiple scales, enhancing our ability to predict and explain these processes transparently.
First, I will introduce my work in AI for personalized health insights. I will present the ENABL Age framework, which integrates AI and XAI to provide precise and interpretable assessments of biological aging. This model not only estimates biological age but also explicates the factors contributing to aging, offering insights for personalized health strategies.
Second, I will discuss my AI innovations in omics data analysis, with a focus on cancer as an age-related disease. I have designed DeepProfile, which analyzes large-scale cancer datasets to identify key biomarkers and pathways, enhancing our approach to precision oncology. Additionally, I have developed StrastiveVI, which further isolates aging-related signals from single-cell transcriptomic data, revealing universal aging patterns and facilitating targeted anti-aging interventions.
Third, the discussion will turn to our pioneering work in the automated generation of plain language, democratizing access to complex biomedical findings and enhancing public health literacy. This work advances biomedical communication by enabling more comprehensible health-related information.
In conclusion, I will outline a vision for future directions in integrating transparent AI with aging research. This effort requires extensive collaboration across biology, clinical science, AI research, public health, and data science. Emphasizing a multidisciplinary approach, we aim to address the complex challenges associated with aging, paving the way for innovative solutions that not only enhance the precision but also the accessibility of healthcare.
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