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In the second, since each microarray dataset involved alpha factor exposure, we grouped these as a single experiment.
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Most existing computational approaches for studying differential gene expression in microarray datasets involve clustering algorithms designed to group genes with similar expression profiles, with the goal of identifying potential annotations for unknown genes [ 10- 17].
The dataset involves 842 nsSNPs.
In order to validate and enrich the DNA microarray dataset, expression of 102 genes involved in early gonad development [ 15] was measured by real-time reverse transcription-polymerase chain reaction (RT-PCR).
The differential colonic expression in UC paralleled that of other genes involved in innate immunity also seen in this microarray dataset, especially the alpha defensins, matrix metalloproteins 3 and 7, IL8, CCL20 and TLR4 [23].
An example workflow would involve using SampleNetwork to pre-process a microarray dataset, then using WGCNA [ 34] to identify modules of co-expressed genes, and finally using ModuleSampleNetwork to explore sample network properties at the modular level.
An independent DNA methylation microarray dataset from TCGA project, in which five CpG sites located in the promoter region of APC were involved, was used to validate the results of the meta-analysis.
Using a previously published microarray dataset [33], we discovered direct engagement and activation of multiple immune pathways by 3O-C12, with 21/30 over-represented pathways related to immune processes involved in cell activation and cytokine production (Table 1).
Here, we set up a bioinformatics platform by integrating three different microarray datasets to reveal key regulators involved in the metastasis of lung cancer.
We used these models to analyze a diabetes microarray dataset.
The protein dataset was further compared to the microarray dataset by extracting the GI numbers from the protein results.
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