If you are a graduate student or post-doc interested in joining the lab, please contact Dr. Mathé directly at “ewy.mathe at osumc dot edu”.


The goal of this lab is to develop and apply analytical methods in genomics, epigenomics, and metabolomics to carefully characterize cancer at a molecular level.  The overall vision of the lab is to significantly ameliorate the process of finding valid candidate biomarkers and therapeutic targets for the diagnosis, prognosis, and treatment of disease, including cancer. Improving such a process would in turn provide a strong foundation for drug discovery efforts.


Our strategy for uncovering novel biomarkers and therapeutic targets is to integrate genomics and metabolomics cancer-specific datasets, and to apply computational biology techniques, such as machine learning and linear modeling, to model robust disease-specific characteristics and clinical outcome.
Leveraging computational biology to integrate genomics, metabolomics, and clinical information is a powerful approach toward this vision and has infinite potential in:

  1. modeling the genomic and metabolomic interactive landscapes of diseases;

  2. predicting effects of perturbing single genes, regulatory elements, and or metabolites on disease and patient outcome;

  3. generating targeted and improved in-silico based hypothesis for experimental validation.

Current Projects include:

  • developing analysis and database methods for integrating genomic and metabolomic data and applying these methods toward molecular and functional characterization of auto-immune diseases and cancer.

  • developing analysis tools for identifying differential enhancer landscapes and applying these methods toward identifying disease-specific enhancers systematically.