Your English writing platform
Free sign upExact(1)
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.
Similar(59)
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.
Besides the cancer expression profiling benchmarks, we have carried out the gene expression experiments on additional disease types in order to further understand the characteristics of the new filter.
We apply our approach to a simple model of gene expression and benchmark the performance.
The obtained expression is benchmarked against a finite-element numerical model of 2-D flow (in r-z coordinates) in an anisotropic medium.
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).
The ideal instruments to evaluate the effect of data pre-processing and the efficacy of different statistical methods on differential expressions are benchmark spike-in experiments [ 4], where a limited number of transcripts are spiked-in at various concentrations in a common mRNA background.
Analysis of microarray expression data and benchmarking.
However, the specific signaling pathways that induce the expression of these benchmark chondrogenic genes remain generally unknown.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com