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Gene network reconstruction from microarray data.

ABSTRACT: BACKGROUND:Often, software available for biological pathways reconstruction rely on literature search to find links between genes. The aim of this study is to reconstruct gene networks from microarray data, using Graphical Gaussian models. RESULTS:The GeneNet R package was applied to the Eadgene chicken infection data set. No significant edges were found for the list of differentially expressed genes between conditions MM8 and MA8. On the other hand, a large number of significant edges were found among 85 differentially expressed genes between conditions MM8 and MM24. CONCLUSION:Many edges were inferred from the microarray data. Most of them could, however, not be validated using other pathway reconstruction software. This was partly due to the fact that a quite large proportion of the differentially expressed genes were not annotated. Further biological validation is therefore needed for these networks, using for example in vitro invalidation of genes.

SUBMITTER: Jaffrezic F

PROVIDER: S-EPMC2712742 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature

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Gene network reconstruction from microarray data.

Jaffrezic Florence F Tosser-Klopp Gwenola G

BMC proceedings 20090716

<h4>Background</h4>Often, software available for biological pathways reconstruction rely on literature search to find links between genes. The aim of this study is to reconstruct gene networks from microarray data, using Graphical Gaussian models.<h4>Results</h4>The GeneNet R package was applied to the Eadgene chicken infection data set. No significant edges were found for the list of differentially expressed genes between conditions MM8 and MA8. On the other hand, a large number of significant ...[more]

PMID: 19615112

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Gene network reconstruction from microarray data.

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Gene network reconstruction from microarray data.

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