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Research Abstracts Online
January 2010 - March 2011

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University of Minnesota Twin Cities
School of Public Health
Division of Biostatistics

PI: Wei Pan

Network-Based Classification and Clustering Methods for Genomic Data

Biological observations reveal that genes in a network tend to function together in biological processes. With the availability of gene pathways or networks and the accumulating knowledge on genes with variants that predispose to diseases (disease genes), these researchers used network-based regression, classification, and clustering methods for high-dimensional microarray gene expression data, SNP data, and other high-throughput genomic data. In addition to theoretical investigations, they conducted simulation studies and real data applications to demonstrate the effectiveness of the proposed methods for practical sample sizes and set-ups.

Group Members

Fang Han, Graduate Student
Benhuai Xie, Collaborator
Hui Zhou, Graduate Student
Yanni Zhu, Graduate Student