This would also enable direct comparison and benchmarking of expression profiles obtained using different technologies.
In the specific case of non-persistent pesticides, namely pyrethroids and organophosphates (OPs), the determination of amounts of a biomarker in 24-h urine collections is generally considered the "benchmark" unit of expression of biomonitoring results [ 1- 3].
GNW was designed for generating in silico benchmarks of gene expression profiles by extracting network modules from prior in vivo studies (such as S. cerevisiae[ 38, 39]) and connecting/expanding these modules to form test networks.
It was not our intention to provide a technical benchmarking of different expression platforms, because such a benchmarking exercise would require controlled microarray, SAGE and EST experiments starting with identical RNA samples derived from the same tissue specimen, and would be beyond the resources of most laboratories.
Two of the sets are known benchmarks of gene-expression experiments.
We have compared a recently developed module-based algorithm, LeMoNe (Learning Module Networks), to a mutual information based direct algorithm, CLR (Context Likelihood of Relatedness), using benchmark expression data and databases of known transcriptional regulatory interactions for Escherichia coli and Saccharomyces cerevisiae.
The method was empirically applied to a suite of ten well-known benchmark gene expression data sets.
In Smith et al [1] the model observer provides a benchmark of the information that the face signals about each expression.
ENNET uses a variety of types of expression data as an input, and shows robust performance across different benchmark networks.
In Table 2, we present a detailed description of these six benchmark microarray gene expression datasets with respect to the number of classes, number of samples, number of genes, and a brief description of each dataset construction.
