While qPCR has some methodological caveats [ 47], it is generally considered a benchmark for gene expression analyses.
In its composition, the Tier 3 roughly corresponds to the target enrichment achievable in a benchmark (traditional) gene expression analysis based on T-test alone.
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.
By using six benchmark microarray gene expression data sets, we compared Recursive Feature Addition with recently developed gene selection methods: Support Vector Machine Recursive Feature Elimination, Leave-One-Out Calculation Sequential Forward Selection and several others.
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.
Five benchmark microarray based gene expression databases are used in this study: GLI-85 (also known as GSE4412 [48], GLA-BRA-180 (also known as GDS1962 [49], CLL-SUB-111 (also known as GSE2466 [50], TOX-171 (also known as GDS2261 [51], and SMK-CAN-187 (also known as GSE4115 [52].
The improved reliability of RCA for estimating expression levels in different organisms and contexts thus makes this index a superior choice for undertaking and benchmarking predictions of gene expression.
To assess our algorithm, we applied SIREN to three benchmark GRNs with associated gene expression data (Additional file 2).
The efficacy of the BALBOA ORF classification technique is first assessed via cross validation and compared to a multi-class k-Nearest Neighbour (kNN) benchmark across three independent gene expression datasets.
We have conducted benchmark experiments to validate gene expression and study function using glypican 3 (Gpc3) a proteoglycan capable of regulating a variety of growth factors [ 22], and to measure cell responses to two important bone growth factors, bone morphogenetic protein 2 (BMP2) and BMP3, which have antagonistic roles in bone growth [ 23, 24].
