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Ebert and Regev have mapped gene expression in blood stem cells as they mature into different types of blood cells.
A classic study mapped gene expression activity in 5 spatial regions across 3 developmental stages [1], yielding 15 spatiotemporal snapshots.
For this, we mapped gene expression data from microarrays onto the corresponding enzymes and their metabolic reaction network.
The aim of this article is to provide a proof-of-concept for the suitability of spatially mapped gene expression for candidate gene prioritization.
We also mapped gene expression patterns in chdA, chdB and chdC nulls that had been induced to synchronously differentiate by cAMP pulsing, to a time point akin to ∼8 hours of development, when cells are chemotactically competent (Fig. 4C).
In order to construct composite networks representing different growth conditions, we have mapped gene expression data onto a parent network of combined protein-protein interactions and gene regulatory interactions (Methods).
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Piro et al. [ 12] proves that spatially mapped gene expressions are suitable for candidate gene prioritization.
This method could also be used to create a reference brain from mapping gene expression patterns and can give detailed images that show neuroanatomical structures such as the fan shaped body and ellipsoid body.
To date, such analyses mostly have been applied to gene-expression microarrays, mapping gene expression QTLs (eQTLs).
Additionally, they provide the two ingredients necessary for mapping gene expression traits to genotype, at moderate cost and without the requirement for previously ascertained genetic variants.
EMAGE database was launched in 2003 (Baldock et al., 2003) in the framework of EMAP project to provide spatially mapped gene-expression data referred only to mouse embryo (Venkataraman et al., 2008).
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