Sentence examples for gene expression benchmarks from inspiring English sources

Seven publicly available microarray-based gene expression benchmarks were used (see in Table 2, where IR is the imbalance ratio) to demonstrate that the proposed framework is potentially capable of selecting the most discriminative candidate genes for phenotype prediction and of finding significant genetic regulation within the selected set of genes.

Using a wide variety of classifiers, notable results are achieved in a set of gene expression benchmarking datasets with subsets of extremely low dimensionality.

Both groups have implemented efficient algorithms that have achieved accuracy levels comparable to the most effective state-of-the-art optimization techniques: Using a naïve Bayes network as the base classifier and the UMDA as the search algorithm, Blanco et al. [ 43] achieve competitive results in two gene expression benchmarking datasets.

Its effectiveness has been validated by using seven well-known cancer gene-expression benchmarks and four other disease experiments, including a comparison to three popular information theoretic filters.

We apply our approach to a simple model of gene expression and benchmark the performance.

These probabilistic models provide estimates for the variance and credibility interval for each transcript and generate more accurate estimates of low levels of gene expression on benchmark datasets when compared with other stochastic models (Text S1).

To assess the accuracy of 3' tag DGE, gene expression levels were benchmarked against MAQC sample TaqMan qPCR data [ 19].

Other bioinformatic benchmarks include protein 3D structure prediction [ 15- 17], protein structure and function prediction [ 18], protein-protein docking [ 19] and gene expression analysis [ 20, 21] benchmarks etc. Benchmark usage varies between different communities.

The method was empirically applied to a suite of ten well-known benchmark gene expression data sets.

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.

While qPCR has some methodological caveats [ 47], it is generally considered a benchmark for gene expression analyses.