Catalyst Awardee

Project Description

An immune aging monitor based on the dynamics of TCR repertoire and machine learning

Shuai Cheng Li, PhD | Department of Computer Science, City University of Hong Hong
Competition Sponsor: Research Grants Council of the Hong Kong Special Administrative Region, China
Award Year: 2023

Human aging is closely associated with changes in the immune system known as immunosenescence. Understanding the underlying mechanisms of immunosenescence is crucial for developing therapies for age-related diseases and for enhancing our understanding of human aging and longevity. However, this presents a significant challenge due to the complexity and heterogeneity of immune cell populations and the difficulty of integrating diverse datasets. Machine learning methods can address these challenges by capturing the key features of human immune aging from high-throughput sequencing data. Our project aims to leverage machine learning methods to identify key biomarkers of immune aging and develop an immune aging monitor. We propose a k-mer-based graph convolutional network framework to fit the relationship between TCR repertoire and age and explore age-associated TCR sequences. We also suggest a bioinformatics pipeline for monitoring immune aging using age-related TCRs and individual medical characteristics to evaluate an immunosenescence score for the TCR repertoire. This monitoring system will provide timely alerts for the excessive aging of the immune system beyond the individual’s actual age and aid medical practitioners in selecting appropriate medical interventions. This project is expected to lead to new insights into immunosenescence and contribute to the development of healthy aging.

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