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Four publicly available GEO Datasets were further analyzed, and the expression data of the genes of interest (GOIs) were compared to those of the present study.
Datasets were further analyzed by the microarray Database system provided by the Center for Information Technology at the NIH.
The two array datasets were further analyzed using K-means clustering (KMC) for genes whose expression pattern was correlated with the induction of fruit abscission (Additional file 3, Table S2).
Since the Anova approach is not ideal for the analysis of several simultaneous measurements, the dataset was further analyzed using multivariate analysis.
Since PCA could not distinguish between sample batches, disease severity defined by either FVC or DLCO, or familial or sporadic cases, the filtered dataset was further analyzed in aggregate.
In the study, the complete genome of Mycobacterium tuberculosis H37Rv was blasted against DEG to identify essential genes and the resulting dataset was further analyzed for similarity search against human genome to identify genes which are not similar with human to avoid host toxicity.
Only those genes showing significant differences in this relatively large dataset were further analyzed for specific complications.
In addition, the significantly up-regulated genes from the differentially expressed genes dataset were further analyzed by investigating the corresponding GO biological processes.
Therefore, given that the Agilent V3 capture kit had the highest overall observed read depth, this dataset (n = 94) was further analyzed to assess the uniformity of coverage of each of the 50 cardiac genes.
The blue channel was further analyzed.
The putative barrier proteome was further analyzed against tabulated categories of a gene ontology database to obtain a functional overview of the blood-labyrinth barrier proteins (Dataset S2).
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