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:

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

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.

Lecture - Translational research, via data integration

Why Data Integration?

A central problem in translational research is that it’s difficult to understand the genetic architecture of a complex disease. Many genome-wide association studies (GWAS) exist—but most of the genetic loci that have been identified have negligible effects: Heritability, in many cases, isn’t found. That’s why we need alternative strategies to find underlying disease causes, said Assistant Professor of Informatics Dokyoon Kim, PhD—and a systems genomics approach, using data integration, can get the job done.

One Math Model to Explain Disease Phenotype

With the traditional approach, you can analyze a beautiful multi-omics data set, Dr. Kim continued—but you must handle the associations one pair at a time. So he asked himself: Can I integrate everything into one mathematical model to predict outcome or explain disease phenotype?