Graph Algorithms for Integrated Biological Analysis, with Applications to Type 1 Diabetes Data - Clustering Challenges in Biological Network - page 206

Chapter 10
Graph Algorithms for Integrated Biological Analysis, with
Applications to Type 1 Diabetes Data
John D. Eblen
Department of Electrical Engineering and Computer Science,
University of Tennessee, Knoxville, TN 37996-3450, USA
Ivan C. Gerling
University of Tennessee Health Science Center,
Memphis, TN, 38163, USA
Arnold M. Saxton
Department of Animal Science,
University of Tennessee, Knoxville, TN 37996-4574, USA
Jian Wu
University of Tennessee Health Science Center,
Memphis, TN, 38163, USA
Jay R. Snoddy
Biomedical Informatics Department,
Vanderbilt University, Nashville, TN 37232, USA
Michael A. Langston ∗
Department of Electrical Engineering and Computer Science,
University of Tennessee, Knoxville, TN 37996-3450, USA
Graph algorithms can be effective tools for analyzing the immense data sets that
frequently arise from high-throughput biological experiments. A major compu-
tational goal is to identify dense subgraphs, from which one can often infer some
form of biological meaning. In this paper, new techniques are devised and ana-
lyzed in an effort to improve the quality and relevance of these subgraphs, and
∗ Corresponding author: [e-mail]
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