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Using three alternative approaches, we repeated the analysis of the maltose induced genes in an automated and un-biased manner to subsequently compare their enrichment results.
To compare the quality of RNA obtained with the two approaches, we repeated the RNA isolation using a multi-step method that pulverizes the bone independently of the phenol-guanidinium based RNA extraction.
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To validate our approach, we repeated the ranking of the four prediction tools but using a traditional approach: experimental signals were assigned to their corresponding nuclei and the differences between experimental and predicted shifts were computed.
To test the robustness of this approach, we repeated the analysis using a nonparametric technique based on fundamentally different assumptions.
Because this is a simulation-based approach, we repeated many draws or iterations and evaluated whether the chain of sample values converged to a stable distribution, which was assumed to be the posterior distribution in which we are interested.
To ensure that our findings were not sensitive to the modelling approach, we repeated the analysis using multinomial regression, which does not impose an order to the categorical outcome.
Additionally, to verify that results from the trend analysis were not affected by differences in the 2011 sampling approach, we repeated the trend analysis on every other sample, and compared the P-values with those of the five year sample.
As a second approach, we repeated this experiment by beginning with equivalent numbers (0.5 × 10) of proliferating DU145 cells and determining the effect of adding additional (0.5 × 10) senescent cells.
To assess the optimality of our approach, we repeated the analyses using only the 1,445 DIS genes (out of the initial 6,151) with known disease phenotype and either sequence mutation or molecular basis known as those declared as truly disease-associated.
It should be noted that for the optimization-based approach, we repeat the experiment eight times to find the best solution.
Indeed the majority of genes found with differential promoter usage appear to have a TE profile similar to the background (Additional file 1: Figure S2A).> In order to examine the robustness of these findings with regards to the alignment and normalisation approaches employed, we repeated the full workflow multiple times using different parameters.
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CEO of Professional Science Editing for Scientists @ prosciediting.com