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We then combined the text based and microarray based networks for each centrality measure.
Thus we built six different drug-gene and disease-gene Boolean interaction networks by utilizing the two subnetworks (text-mining based and microarray based) extracted for each drug and disease.
Indeed, one of the strengths of the approach used here is the use of two distinct sources, sequence based and microarray based, of high-throughput gene expression data.
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Current approaches in functional genomics based on protein- protein interaction and microarray based transcription profiling are helping to decode this mystery.
SEF and TCM carried out microarray based gene expression analysis and participated in drafting the manuscript.
Two different databases were used to generate microarray based drug-gene and disease-gene interactions.
Examples of this approach include microarray based genome-wide expression analyses [15] and genotype detection for specific gene [16].
Microarray based studies require clear objectives for selecting cases and appropriate analysis methods.
Since microarrays measure the level of expression between genes and can be utilized to understand functional relationship between genes, we sought to use a functional gene-gene network to generate microarray based network.
Consistent with previous reports we found qRT-PCR based platforms to have higher sensitivity than microarray based platforms [ 12].
Sequencing-based and microarray-based high-throughput detection of DNA methylation approaches are widely used in genome-wide methylation studies.
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