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Research Abstracts Online
January 2008 - March 2009

University of Minnesota Twin Cities
Institute of Technology
Department of Computer Science and Engineering

PI: George Karypis, Associate Fellow

Scalable Algorithms for Graph Partitioning, Data Mining, Bioinformatics, and Cheminformatics

The Karypis group is developing computational techniques in computational biology, bioinformatics, and cheminformatics. This research focuses on five areas: developing better machine learning algorithms based on support vector machines by designing kernel functions that capture the characteristics of proteins and chemical compounds and improving their scalability; developing better scoring methods for sequence alignment, sequence-structure search methods, and statistically derived potential functions that are designed to capture the sequence-structure conservation present in protein sequences; developing better feature extraction algorithms for chemical compounds that utilize topological and geometric substructures; developing better machine learning algorithms for chemical compound classification, structure-activity relationship modeling, target fishing, and target hopping; and developing machine learning and data mining algorithms for compound synthesizability prediction and compound library design. This work builds on the group’s earlier work on highly effective and scalable graph partitioning algorithms, clustering algorithms for high-dimensional datasets and scalable-pattern discovery algorithms, and developing algorithms for protein structure prediction, remote homology prediction, and rational drug design.

Group Members

Rezwan Ahmed, Graduate Student
Kevin DeRonne, Graduate Student
Christopher Kauffman, Graduate Student
Xia Ning, Graduate Student
Yevgeniy Podolyan, Graduate Student
Huzefa Rangwala, Graduate Student
Nikil Wale, Graduate Student