Aging research, driven by big data analytics and artificial intelligence (AI), has become a globally prominent field. This work focuses on two core pillars of aging science—biological age prediction and systematic assessment of human aging/mortality risk—while introducing an AI-driven knowledge graph platform to reveal novel insights into aging and longevity mechanisms. First, we developed a composite machine learning-based biological age (ML-BA) model using biomarkers from clinical data; integrating multiple algorithms, it predicts biological age more accurately than single-algorithm approaches, correlates strongly with health risk indicators (e.g., metabolic markers) and age-related diseases (e.g., cardiovascular disease), and enhances aging measurement precision. Second, leveraging millions of de-identified healthcare records, we built an interpretable AI model for aging trajectory and mortality risk assessment; its parameters serve as reliable mortality risk references and provide actionable healthy aging guidelines. We also integrated our work with the HALDxAI Database (Human Aging and Longevity Database with AI)—an AI/knowledge graph platform analyzing genes, proteins, compounds, and their interactions—offering multi-dimensional visualization, AI-powered precise retrieval, and systematic relationship tracing for aging-related molecular exploration. Collectively, our approach integrates state-of-the-art techniques to advance aging research from clinical measurement to molecular mechanisms, providing assessment tools, accelerating longevity insights, and laying groundwork for precision healthy aging interventions.

Prof. Ming Chen received his PhD in Bioinformatics from Bielefeld University, Germany, in 2004. Currently he is working as a full Professor in Bioinformatics at College of Life Sciences, Zhejiang University. His group research work mainly focuses on, computational and functional analysis of transcriptomics, systems biology, and generally bioinformatics education, research and application. Prof. Chen is serving as an academic leader in Bioinformatics at Zhejiang University. He chairs the Bioinformatics society of Zhejiang Province, China. He serves as a committee member of Chinese societies for “Modelling and Simulation of Biological Systems”, “Computational Systems Biology”, “Functional Genomics & Systems Biology”, and “Biomedical Information Technology”. More info is available via his personal webpage:  http://bis.zju.edu.cn/binfo/members/ming_chen.htm