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2012 IEEE International Conference on Bioinformatics and Biomedicine (2012)
Philadelphia, PA, USA USA
Oct. 4, 2012 to Oct. 7, 2012
ISBN: 978-1-4673-2559-2
pp: 1-5
Kathryn Dempsey , College of Information Science and Technology, University of Nebraska at Omaha
Tzu-Yi Chen , Department of Computer Science, Pomona College
Sanjukta Bhowmick , College of Information Science and Technology, University of Nebraska at Omaha
Hesham Ali , College of Information Science and Technology, University of Nebraska at Omaha
ABSTRACT
Network modeling of biological systems is a powerful tool for analysis of high-throughput datasets by computational systems biologists. Integration of networks to form a heterogeneous model requires that each network be as noise-free as possible while still containing relevant biological information. In earlier work, we have shown that the graph theoretic properties of gene correlation networks can be used to highlight and maintain important structures such as high degree nodes, clusters, and critical links between sparse network branches while reducing noise. In this paper, we propose the design of advanced network filters using structurally related graph theoretic properties. While spanning trees and chordal subgraphs provide filters with special advantages, we hypothesize that a hybrid subgraph sampling method will allow for the design of a more effective filter preserving key properties in biological networks. That the proposed approach allows us to optimize a number of parameters associated with the filtering process which in turn improves upon the identification of essential genes in mouse aging networks.
INDEX TERMS
clusters, biological networks, network filters, chordal graphs, spanning tree, lethal genes, hubs
CITATION

K. Dempsey, T. Chen, S. Bhowmick and H. Ali, "On the design of advanced filters for biological networks using graph theoretic properties," 2012 IEEE International Conference on Bioinformatics and Biomedicine(BIBM), Philadelphia, PA, USA USA, 2012, pp. 1-5.
doi:10.1109/BIBM.2012.6392617
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