Ming Chen*
College of Life Sciences, Zhejiang University, Hangzhou, China
mchen [at] zju.edu.cn
Abstract
Chronic diseases and aging have become a global public health burden, demanding innovative approaches to improve early assessment, decode hallmark mechanisms, and advance personalized treatment. With the explosion of multi-omics data and the advancement of artificial intelligence (AI) technologies, bioinformatics engineering has emerged as a transformative force bridging biomedical discoveries and clinical applications for chronic diseases.
This talk introduces innovations in AI-driven biomedical and bioinformatics engineering for chronic disease management. We developed BioAgeX and HALDxAI, which integrate multi-omics data and clinical records to enable early risk stratification, accurate diagnosis of biological age, and even mortality risk prediction, overcoming the limitations of traditional single-dimensional assessment. We propose AI-driven knowledge graph approaches, including decoding disease-related molecular regulatory networks, identifying novel biomarkers, providing intervention treatment responses for chronic conditions, and optimizing precision therapeutic regimens. Furthermore, we discuss challenges and future directions in this interdisciplinary field, such as data integration, model interpretability, and the translation of AI-driven discoveries.
Keywords: aging, AI, integration, knowledge graph