OVERVIEW

Our research focuses on the development and application of advanced data integration methods to enhance the diagnosis, treatment, and prevention of complex diseases. A key aspect of our work involves integrating multi-omics data with biological knowledge to translate genomic and phenotypic data from electronic health records (EHR) into actionable clinical products. Our projects span both theoretical and applied research, including:

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• Developing novel data integration methods that combine multi-omics using machine learning and deep learning approaches

• Predicting clinical outcomes by analyzing interactions between multi-omic features

• Integrating multi-modal imaging with multi-omics data

• Identifying gene-by-environment (GxE) interactions across various phenotypes and diseases

• Constructing disease-disease networks using EHR-linked biobank data

• Developing Immune health scores and foundation models for Immune Health

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Our ongoing work continues to focus on providing clinical tools that leverage the interactions within and between different layers of genomic data. Our long-term research goal is to develop and evaluate sophisticated data integration methods that simultaneously account for individual variations in genomic (multi-omics) data, imaging data, phenotype data from EHRs, and environmental/lifelog data, all aimed at advancing precision medicine. Additionally, our research leverages artificial intelligence (AI)-driven approaches to further enhance healthcare outcomes, paving the way for more personalized and effective treatments.