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Recently, numerous high throughput sequencing technologies have been developed to allow rapid and cost-effective ways to obtain genome-wide information [35] [37].
Hence fungi are the objective in numerous high throughput screening (HTS) programs targeting new pharmaceutics and other bioactive components [ 7– 10].
Advantages of a genome-wide approach in gene network analysis have been proven by numerous high throughput microarray experiments and data modeling [ 14].
Over the last decade, kinases have emerged as attractive therapeutic targets for a number of different diseases, and numerous high throughput screening efforts in the pharmaceutical community are directed towards discovery of compounds that regulate kinase function.
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In response, numerous high-throughput (HT) strategies are being developed to analyze biomaterials and their interactions with cells using combinatorial approaches.
We introduce GenRev, a network-based software package developed to explore the functional relevance of genes generated as an intermediate result from numerous high-throughput technologies.
Numerous high-throughput studies querying MYC regulated genes have been published [22] [29], [57].
Numerous high-throughput studies querying MYC regulated genes have recently been published [22] [32].
Numerous high-throughput data including genomics, transcriptomics, proteomics and metabolomics data are available for soybean.
In particular, the issues presented are quite pertinent with the advent of numerous high-throughput sequencing studies.
If we perform numerous high-throughput experiments even on the same cell line, each experiment can give rise to different measurements.
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