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 health care 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. It uses the power of computational predictive genomics to advance pressing societal issues such as enabling patient-specific cancer treatment, determining phenotype from a person's or organism's genotype, understanding and modifying microbiomes, and meeting the rapidly expanding need for food. CCBGM offers: An approach to Big Data problems in genomic biology that comprehensively spans all of its key elements, including analytics, computing, and generation of actionable intelligence. Biological expertise (e.g., human genomics, crop and animal sciences) combined with technical expertise in algorithms and computing systems (e.g., high performance computing, cloud, and special-purpose acceleration). A strong track record of working with industry in the multidisciplinary domains of computing, biotechnology, and life sciences. Access to multidisciplinary faculty, clinicians, and students working in bioinformatics, genomic applications, security, health sciences, and computing systems and algorithms.
Research Areas
These following topics leverage the multidisciplinary capabilities of the CCBGM team to focus on clinical knowledge in human patients. However, the methods, tools, and algorithms developed as part of these efforts (e.g., microbiome, compression, imaging, genomic security, and acceleration) also apply in the broader context of analyzing the sequence data of crops, animals, and other organisms.
Actionable intelligence
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., multiomics, clinical, and microbiome) and identify statistically significant mutations, genes, metabolites, pathways, and networks that are associated with clinical or functional outcomes.
Computing and data management
This research area 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
Systems innovation research addresses the design and implementation of specialized computer systems to efficiently and accurately execute the algorithms for mining actionable intelligence from multiomics data. CCBGM's application-specific computing systems will have the ability to:
Efficiently handle storage and retrieval of large quantities of data produced in sequencing experiments as well as a body of medical information that maintains known correlations between genomic variants, genes, pathways, and human diseases.
Efficiently compute complex statistical analyses and machine-learning algorithms on parallel-processing platforms such as graphics processing units and field programmable gate arrays, as well as scale out to utilize large warehouse-scale computers (clouds, supercomputers).
CCBGM designs will also address constant evaluation, monitoring, and quality control of algorithms, workflows, and systems, which will provide the flexibility to incorporate new data, statistical models, and algorithms as they become available.
Facilities & Resources
Partner Organizations
Abbreviation |
CCBGM
|
Country |
United States
|
Region |
Americas
|
Primary Language |
English
|
Evidence of Intl Collaboration? |
|
Industry engagement required? |
Associated Funding Agencies |
Contact Name |
Ravishankar K. Iyer
|
Contact Title |
Center Director
|
Contact E-Mail |
rkiyer@illinois.edu
|
Website |
|
General E-mail |
|
Phone |
|
Address |
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 health care 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. It uses the power of computational predictive genomics to advance pressing societal issues such as enabling patient-specific cancer treatment, determining phenotype from a person's or organism's genotype, understanding and modifying microbiomes, and meeting the rapidly expanding need for food. CCBGM offers: An approach to Big Data problems in genomic biology that comprehensively spans all of its key elements, including analytics, computing, and generation of actionable intelligence. Biological expertise (e.g., human genomics, crop and animal sciences) combined with technical expertise in algorithms and computing systems (e.g., high performance computing, cloud, and special-purpose acceleration). A strong track record of working with industry in the multidisciplinary domains of computing, biotechnology, and life sciences. Access to multidisciplinary faculty, clinicians, and students working in bioinformatics, genomic applications, security, health sciences, and computing systems and algorithms.
Abbreviation |
CCBGM
|
Country |
United States
|
Region |
Americas
|
Primary Language |
English
|
Evidence of Intl Collaboration? |
|
Industry engagement required? |
Associated Funding Agencies |
Contact Name |
Ravishankar K. Iyer
|
Contact Title |
Center Director
|
Contact E-Mail |
rkiyer@illinois.edu
|
Website |
|
General E-mail |
|
Phone |
|
Address |
Research Areas
These following topics leverage the multidisciplinary capabilities of the CCBGM team to focus on clinical knowledge in human patients. However, the methods, tools, and algorithms developed as part of these efforts (e.g., microbiome, compression, imaging, genomic security, and acceleration) also apply in the broader context of analyzing the sequence data of crops, animals, and other organisms.
Actionable intelligence
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., multiomics, clinical, and microbiome) and identify statistically significant mutations, genes, metabolites, pathways, and networks that are associated with clinical or functional outcomes.
Computing and data management
This research area 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
Systems innovation research addresses the design and implementation of specialized computer systems to efficiently and accurately execute the algorithms for mining actionable intelligence from multiomics data. CCBGM's application-specific computing systems will have the ability to:
Efficiently handle storage and retrieval of large quantities of data produced in sequencing experiments as well as a body of medical information that maintains known correlations between genomic variants, genes, pathways, and human diseases.
Efficiently compute complex statistical analyses and machine-learning algorithms on parallel-processing platforms such as graphics processing units and field programmable gate arrays, as well as scale out to utilize large warehouse-scale computers (clouds, supercomputers).
CCBGM designs will also address constant evaluation, monitoring, and quality control of algorithms, workflows, and systems, which will provide the flexibility to incorporate new data, statistical models, and algorithms as they become available.