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Currently, high throughput data of this type do not exist.
Comparison of high- throughput data sets.
The emergence of high throughput data has facilitated the study of patterns of transcription.
Network analysis has been increasingly utilized in interpreting high throughput data.
As it was mentioned, drawings are used as a background for high throughput data visualization.
We developed network-based methodology for integrative analysis of high throughput data sets of different types.
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Bioinformatics approaches, microarray experiments, as well as other high-throughput data are prone to false-positives.
Today, individual experiments have been partly replaced by high-throughput data generation methods.
On real high-throughput data, both take few seconds to treat tens of thousands of genes.
Bioinformatic enrichment tools are a powerful approach to identify biological processes in high-throughput data analysis.
High-throughput data tend to be worse than low-throughput data [ 29].
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