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Results from these analyses demonstrate significantly higher levels of IL8 expression in the basal compared to the luminal A subtype in both datasets, which was also seen in the ERBB2 subtype in the UPPSALA dataset.
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These two datasets are the Australian and Japanese datasets, which are also available from the UCI Machine Repository.
The best results obtained by k-means refer to "Corel" datasets, which is also the best results for our approach (i.e. ASTS) when considering k equals to 4 and 8.
To establish how well our panel performed for ccRCC/normal-kidney discrimination we used a previously published dataset which was also used by two other groups [ 2, 3] in testing their gene lists to distinguish ccRCC from normal kidney.
Greater elucidation may well be achieved from considering the entire dataset, which is also recommended for the individual height classes.
This may reflect high redundancy of the flag leaf dataset, which is also obvious for grains (62,467 annotated sequences) but not for glumes (40,616 annotated sequences).
The same method for incorporating fiber vectors aligned with the axial direction of a cylindrical structure was also used to assign fiber orientation to the larger trabeculations present in the dataset, which were also tagged as described in Segmentation postprocessing.
Given that there were over 110 scaffolds present in a dataset with only 157 inhibitors, we consider this dataset to be highly diverse, which was also one of its original design principles.
Which was also regrettable.
Which was also a disaster.
Although these methods (except WAD) were inferior to the t-statistic-based methods when the results for the older spike-in dataset (Dataset 1, which is obtained from the HG-U95A array) were compared, they were better for both the newer spike-in dataset (Dataset 2, which is from the HG-U133A array) and the 36 real experimental datasets (Datasets 3 38, which is also from the HG-U133A array).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com