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Extensive simulations on artificial data and application of our module network approach to infer the signaling network between 13 genes in the ER- α pathway in human MCF-7 breast cancer cells show that our approach gives sensible results.
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For example, the Module Networks approach built a probabilistic model for the gene expression correlations between regulators and target genes and iteratively searched for the most compatible partition of targets genes to their respective regulators [ 9].
Segal et al. [ 22] have developed a module-network approach to identify modules that underlie tumourigenesis.
We compared the module network with the triplets inference approach described in Markowetz et al. (2007).
With the stage-wise HCC expression data, the proposed differential co-expression network approach resolved modules with coherent co-expression change patterns over all HCC developmental stages and even deciphered the temporal activation/dismissal of involved functions.
Those results corroborate module network inference as a robust and useful approach to gain more precise insights into miRNA function.
We then proposed a differential co-expression network approach to uncover network modules involved in HCC development.
We then proposed a differential co-expression network approach to uncover gene modules involved in HCC development.
Systems approaches, like the inference of a module network from expression data, can help to achieve this goal.
Utilizing this integrative network approach, we identified transcript co-expression modules correlated with SI and other related metabolic phenotypes in each ethnic group.
The network approach allows us to explore a set of interacting genes measured by modules or sub-networks that are involved in a complex disease like PCa.
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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