Talk Title: "Multi-omics Data Integration For Translational Bioinformatics"
Dokyoon Kim (Geisinger Health Systems)
Recent multi-omics data and clinical information emerging from a large-scale collaborative initiative, such as The Cancer Genome Atlas (TCGA), have provided exceptional opportunities to investigate the complex genetic basis of disease for improving the ability to diagnose, treat, and prevent cancer. The ultimate utility of our significant investment in data generation will largely depend on analytical strategies and study designs that allow for the integration of multiple data sources into the analytical framework. Thus, it is particularly essential to develop a novel methodological framework to better predict clinical outcomes, further exploring a global view on the interactions within/between different dimensional genomic data. In this talk, we will discuss novel data integration methods that combine multi-omics data and biological knowledge for predicting cancer clinical outcomes using TCGA dataset. Integrating genomic data and phenotype data derived from electronic health record (EHR) using DiscovEHR cohort will be also discussed. My long-term research goal is to develop and evaluate sophisticated data integration methods that simultaneously combine peoples’ individual variations in genomic (‘omic) data, imaging data, phenotype data from EHR, and environment/lifelog data for improved precision medicine