Exploring the Intersection of AI and Aging Biology

🧠AI can be used to model the biological and phenotypic dynamics of aging and age-related diseases.

🔬Deep phenotyping and multimodal data analysis are key areas of interest.

⏲️Temporal models based on AI and machine learning can reveal changes over time during aging or disease processes.

💡Interventions for healthy aging and candidate drug target discovery for AD/ADRD are important research themes.

🔑Successful proposals require established access to relevant data, either through private partnerships or public resources.

What are some suggested research themes for AI and aging biology?

—Some suggested research themes include deep phenotyping, machine learning analysis of aging hallmarks, impact of metabolic changes on aging, and AI-derived patient subtyping.

What type of applications are appropriate for AI and aging biology?

—Applications that use AI to model the biological and phenotypic dynamics of aging and age-related diseases are appropriate.

What is the importance of data access in these projects?

—Data access is crucial, and successful proposals should have established access to relevant data, either private or public.

Is this research clinically focused?

—Yes, this research has direct relevance to aging and age-related diseases and is clinically focused.

Are there publicly available resources for data analysis?

—Yes, there are publicly available resources such as UK Biobank and big single-cell omic data sets that can be used for analysis.

12:58Dr. Sap discusses the potential of AI in understanding the biology of aging.

13:40Dr. Battle highlights the focus areas of the AITC program in AI and aging biology.

14:34Dr. Battle suggests specific research themes and emphasizes the importance of data access.