Motivation: The investigation of topological modifications of the gene interaction networks in cancer cells is essential for understanding the desease. We study gene interaction networks in various human cancer cells with the random matrix theory. This study is based on the Cancer Network Galaxy (TCNG) database which is the repository of huge gene interactions inferred by Bayesian network algorithms from 256 microarray experimental data downloaded from NCBI GEO . The original GEO data are provided by the high-throughput microarray expression experiments on various human cancer cells. We apply the random matrix theory to the computationally inferred gene interaction networks in TCNG in order to detect the universality in the topology of the gene interaction networks in cancer cells . Results: We found the universal behavior in almost one half of the 256 gene interaction networks in TCNG. The distribution of nearest neighbor level spacing of the gene interaction matrix becomes the Wigner distribution when the network is large (condensed) , and it behaves as Poisson distributionwhen the network is smaller. We also observe the transition between the Poisson and the Wigner distributions as the threshold of confidence factor of the gene interactions is shifted. We expect that the random matrix theory provides an effective analytical method for investigating the huge interaction networks of the various transcripts in cancer cells .
Investigations of topological uniqueness of gene interaction networks in cancer cells are essential for understanding the disease. Based on the random matrix theory, we study the distribution of the nearest neighbor level spacings P(s) of interaction matrices for gene networks in human cancer cells . The interaction matrices are formed using the Cancer Network Galaxy (TCNG) database , which is a repository of gene interactions inferred by a Bayesian network model. In TCNG database, 256 NCBI GEO entries regarding gene expressions in human cancer cells were selected for the Bayesian network calculations. We observe the Wigner distribution of P(s) when the gene networks are dense networks that have large numbers of edges. In the opposite case, when the networks have small numbers of edges, P(s) becomes the Poisson distribution . We investigate relevance of P(s) both to the size of the networks and to edge frequencies that manifest reliance of the inferred gene interactions .
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Motivation: The investigation of topological modifications of the
Investigations of topological uniqueness of
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is
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desease. We study gene interaction networks in various
disease. Based on the random matrix theory, we study the distribution of the nearest neighbor level spacings P(s) of interaction matrices for gene networks in
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with the random matrix theory. This study is based on
. The interaction matrices are formed using
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which is the repository of huge
, which is a repository of
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Bayesian network algorithms from
a Bayesian network model. In TCNG database,
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microarray experimental data downloaded from NCBI GEO . The original GEO data are provided by the high-throughput microarray expression experiments on various human cancer cells. We apply the random matrix theory to the computationally inferred gene interaction networks in TCNG in order to detect the universality in the topology of the gene interaction networks in cancer cells
NCBI GEO entries regarding gene expressions in human cancer cells were selected for the Bayesian network calculations. We observe the Wigner distribution of P(s) when the gene networks are dense networks that have large numbers of edges. In the opposite case, when the networks have small numbers of edges, P(s) becomes the Poisson distribution
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Results: We found the universal behavior in almost one half of
We investigate relevance of P(s) both to the size of the networks and to edge frequencies that manifest reliance of
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256 gene interaction networks in TCNG. The distribution of nearest neighbor level spacing of the gene interaction matrix becomes the Wigner distribution when the network is large (condensed) , and it behaves as Poisson distributionwhen the network is smaller. We also observe the transition between the Poisson and the Wigner distributions as the threshold of confidence factor of the gene interactions is shifted. We expect that the random matrix theory provides an effective analytical method for investigating the huge interaction networks of the various transcripts in cancer cells
inferred gene interactions