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To further pinpoint the interaction module, we generated three C-terminal deletions (see Fig. 1d).
Using the morphology module, we generated crystal structures with faces corresponding to the PXRD data.
To confirm the hypothesis that the catalytic function of CBS-1 is mediated only by its C-terminal module, we generated individual CBS-1 modules in E. coli.
By hunting in the module of interest for transcriptional regulators (DNA binding transcription factors and co-factors), or asking the related question "which transcriptional regulator has the highest absolute, average correlation to all the genes in the module?", we generated a ranked list of regulators predicted to control the processes in question.
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To compute the significant of finding specific COGs functional modules, we generated 10,000 random module sets of the same size, and counted the number of times we found each COGs module in each randomized network.
In order to obtain a null model of the dendrogram clustering error that corresponds to the random clustering of network modules, we generate reference networks with permuted module labels.
For each real module s with ns nodes, we generated the random modules by randomly selecting a set of ns nodes from the whole network.
To find coexpression modules, we first generated network graphs using different PCC cutoff values.
To study how the different modularity measures depend on the number of modules in the network as well as the strength of the module's interconnectivity, we generate networks with a tunable amount of modularity.
For two phenotype modules, we firstly randomly generated two modules with the same number of disease genes.
Using expression values from the van de Vijver dataset, we used modules generated by all three algorithms to construct expression modules and used them to predict prognosis.
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