Data Discovery

The goal of this project is to generate actionable intelligence using smart analytics to integrate big-data in the form of omics (genomics, transcriptomics, metabolomics etc.), clinical data and longitudinal data from electronic health records (EHR). The actionable intelligence is a descriptive piece of information with high-confidence and accuracy that can be used to tailor and individualize diagnosis and therapeutics for a given patient or inform potential candidates for biomarker discovery. The analytics and tools will be developed using engineering expertise at the Univ. of Illinois in close collaboration and partnership with leading clinicians, biologists and bioinformatics specialists at Mayo Clinic. This project will explore societally relevant, prevalent and yet less-understood diseases such as triple-negative breast cancer, major depressive disorder and diabetes.

This project will improve the robustness of error analysis and correction by developing new root-cause error analysis methods and hybrid error-correction algorithms accelerated via FPGA or GPU implementations. Variant calling will be improved by both suppressing errors and building new machine-learning based techniques that would work with clinical workflows.

We will develop and translate the use of nutritional interventions for improving human health and for management practices for improved agricultural production. Data-analysis approaches from our study will provide the industry and broader scientific community with a valuable resource to address many questions about host-microbe interactions in the health sector, resulting in development of new diagnostic tools, therapeutic strategies, and targeted probiotic interventions.