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About CBI
Now more than ever before, the power to turn huge volumes of data into information about our world has put the answers to some of life’s most challenging questions within our grasp. The George Washington University’s Computational Biology Institute (CBI) brings together leading faculty in biology, medicine and computing to harness this information, opening new doors of discovery that have the potential to benefit millions. CBI is also maximizing the university’s unique relationships in the nation’s capital to form research partnerships and spotlight cutting-‐edge topics that enhance human and environmental health. By blending its own skills with the expertise of engineers, mathematicians, statisticians, clinicians and others, the CBI is contributing to knowledge and resources that are used by researchers on a global scale, influencing how the world uses science and technology to solve its most pressing problems. With these truly incomparable resources and expertise, the CBI performs cutting-edge research and helps raise awareness of scientific advancements that improve our health, environment and overall quality of life.
RANKED #11
U.S. News & World Report's List of Best Public Health Graduate Programs
LOCATION
Washington D.C.'s only Public Health School
RESEARCH
Shaping Public Health Policy and Practice
FACULTY
130 Full-time Faculty Leading our Students, and 300 Part-time Faculty.
Featured Publications
Estimating rare disease prevalence and costs in the USA: a cohort study approach using the Healthcare Cost Institute claims data
April 24, 2025
Estimating rare disease prevalence and costs in the USA: a cohort study approach using the Healthcare Cost Institute claims data
Induction of mastitis by cow-to-mouse fecal and milk microbiota transplantation causes microbiome dysbiosis and genomic functional perturbation in mice
April 24, 2025
To understand the host-tropism phenomena of bovine-tropic mastitis microbiomes, we developed a cow-to-mouse mastitis model.
Differential expression of single-cell RNA-seq data using Tweedie models
April 24, 2025
To model the technological variability in cross-platform scRNA-seq data, here we propose to use Tweedie generalized linear models.
Chromosome-level genome assemblies of the malaria vectors Anopheles coluzzii and Anopheles arabiensis
April 24, 2025
We produced chromosome-level genome assemblies for A. coluzzii and A. arabiensis using Oxford Nanopore sequencing technology and Hi-C scaffolding approach.
