The Center for Computational Biotechnology and Genomic Medicine (CCBGM) leverages the power of data analytics, artificial intelligence, machine learning, and high-performance computation to advance healthcare discovery. To do this, CCBGM combines research insights in engineering and genomic biology with the world-renowned expertise in individualized medicine and clinical research and practice of the Mayo Clinic.
Ravishankar K. Iyer
Center Director - University of Illinois
+1 217 333 7774
Center Director - Mayo
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Melissa Minter Baerg
Research Program Manager - Mayo
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Research Program Manager - University of Illinois
+1 217 244 9527
This research area looks at the translation of Big Data to clinical knowledge. The overarching goal is to enhance patient-specific understanding of disease to tailor diagnoses and individualized treatment. Projects in this thematic component develop technologies to identify and classify genomic variants, genes, and drivers for human disease. Specifically, CCBGM develops algorithms to help merge heterogeneous datasets (e.g., multi-omics, clinical, and microbiome) and identify statistically significant mutations, genes, metabolites, pathways, and networks associated with clinical or functional outcomes.
The application of genomics across the life sciences industry has been challenged by an inadequate ability to generate, interpret, and apply genomic data quickly and accurately for a wide variety of applications. The University of Illinois at Urbana-Champaign and the Mayo Clinic created a collaborative research center called the Center for Computational Biotechnology and Genomic Medicine (CCBGM). This Center brings the engineering resources of the University of Illinois and the Mayo Clinic in computational and biological sciences together with a broad range of industry partners to perform collaborative research in genomics and personalized medicine of interest to industry and academia.
The goal of the CCBGM has expanded beyond genomics. It now includes leveraging the power of data analytics, artificial intelligence, machine learning, and high-performance computation to advance big data technologies to the point that we can fully analyze and exploit the massive amounts of genomic, multi-omic, and biological data that are now available. To accomplish this, there is a need for computational tools for "big data" in genomic and genetic data analysis, biochemistry, imaging, compression, encryption, and data transfer. The Center addresses those needs using biological modeling, algorithm design, interface development, and iterative optimization in direct collaboration with cutting-edge biology researchers analyzing real data.
These tools are helping medical institutions, like the Mayo Clinic and others in the healthcare industry, to develop and understand biomarkers and data analytics for better individualized diagnosis, prevention, and treatment. With the guidance of the CCBGM Industry Advisory Board, we have developed a research roadmap that meets our membership's evolving interests and needs. While early projects were heavily weighted toward hardware, software, and analytics, our evolving focus now includes health data analytics, artificial intelligence, and data security. However, our center goals are still developing actionable intelligence and system innovations. Projects have focused on patient-specific research, including cancer genomics, neurological disorders, and depression.
With focuses on innovations in security, storage, and compression technologies for patient-specific and genomic data. Such methods are required to process and understand large-scale bioinformatics problems.
Systems innovation research addresses designing and implementing specialized computer systems to efficiently and accurately execute the algorithms for mining actionable intelligence from multi-omics data. CCBGM's application-specific computing systems will have the ability to:
CCBGM designs will also address constant evaluation, monitoring, and quality control of algorithms, workflows, and systems, providing the flexibility to incorporate new data, statistical models, and algorithms as they become available. The University of Illinois and Mayo Clinic are leveraging the power of data analytics, artificial intelligence, machine learning, and high-performance computation to advance healthcare discovery.