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Home > Research > Research Areas > Bioinformatics & Computational Biology

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.

Faculty

  • Daniel Boley
  • John Carlis
  • Ravi Janardan
  • George Karypis
  • Rui Kuang
  • Vipin Kumar
  • Chad Myers

Labs and Selected Projects

  • Bioinformatics George Karypis
  • Bioinformatics and Computational Biology Vipin Kumar
  • Chemical Informatics George Karypis
  • Computational Biology Rui Kuang
  • Karypis Lab
  • Principal Research Projects Daniel Boley
  • Research on Protein Structures Rui Kuang

Related Centers and Programs

  • CBCB: Consortium for Bioinformatics and Computational Biology
  • Computational Neuroscience in the Graduate Program in Neuroscience
  • Graduate Program in Bioinformatics

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  • Last modified on July 23, 2008