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Traditionally, gene signatures are statistically deduced from large gene expression and proteomics datasets and have been applied as an experimental molecular diagnostic technique that is sensitive to experimental design and statistical treatment.
Other large gene expression databases like Genelogic's gene expression database may be more robust but are unavailable to the public [28].
Our results demonstrate the power of an integrative, systematic approach to the analysis of a large gene expression dataset to uncover underlying structure, and inform future eQTL studies.
We used a large gene expression profile dataset for mature human B cells, which may have both improved our ability to characterized some TFs as well as hinder the ability to characterize others.
In order to independently confirm those results, we re-analyzed two publicly available large gene expression datasets, totalling 58 paired lesional and non-lesional skin samples, for the expression of all PPAR isotypes.
Utilization of large gene expression sets, suffer from other problems.
The increasing availability of large gene expression cancer datasets presents unprecedented opportunities for translational advances.
Ample genome annotation is essential for interpreting large gene expression datasets [ 21].
Finally, we give results of applying HappieClust on a large gene expression dataset.
For both genes, no large gene expression differences were observed between the two strains.
Motivation: Meta-analysis of large gene expression datasets obtained from public repositories requires consistently annotated data.
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