Bioinformatics and Computational Biology

Research in this area explores the use of computational methods to better categorize, visualize, and model biological data and systems. These problems often involve massive, high-dimensional datasets and their solutions draw from many disciplines of computer science including database management, data mining, machine learning, and algorithmic optimization. Research in this group includes algorithms for sequence and structure analysis, protein structure prediction, virtual screening and lead discovery, data modeling of scientific applications, DBMS (database management system) extensions in support of brain image and proteomics analyses, and building predictive models for effective disease diagnoses. The research group has increasing collaboration with the College of Biological Sciences, the Medical School, and the Mayo Clinic.


Daniel Boley
John V. Carlis
Ravi Janardan
George Karypis
Dan Knights
Rui Kuang
Vipin Kumar
Chad L. Myers
Catherine Qi Zhao