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In none of these three cases are the results of the present analysis congruent with recent molecular analyses (e.g., [27], [29]), but strongly conflicting signals in the morphological dataset appear to be absent.
The results of the partitioned analyses (Figures 4 6) suggest that the phylogenetic signal driving their union as sister taxa and derived placement relative to other ciconiiforms may be predominantly coming from characters in the hindlimb (e.g., characters 337, 384, 393, 415, 418), though few characters in the present dataset appear to be unique to these two taxa.
The majority of interactions in this dataset appear to be between promoters and other promoters (N = 21694), with a large number of promoter:enhancer interactions (N = 4177) (see Fig. 3b).
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Given the size of the dataset however, manually checking and correcting the entire dataset appeared to be impractical.
According to these values, the dataset appears to be unbalanced, i.e. the number of samples varies greatly from one class to another.
As shown in Table 5, the observed performance of the three prediction methods on HLA-DRB1*0301 MHCBN-UPDS version of this dataset appears to be overly optimistic relative to that on its similarity-reduced and weighted counterparts.
The pattern of lost and gained domain architectures in the TAIR7 dataset appears to be very similar to that in TAIR8 dataset (Additional file 9, Table S8).
While other evaluation methods have been devised [ 16, 35], computing the statistics above per residue and averaging across the dataset appears to be the most objective and easily comparable method.
While the dataset appears to be more up-to-date than that of other reviews, further primary studies assessing HPV prevalence in non-cervical cancers in European populations continue to be published, and should ideally be incorporated in further reviews and meta-analyses on this topic.
As shown in Table 1, the AUC values on most of the datasets appear to be similar for the four methods.
Surprisingly, on the level of interactions between functional groups, prokaryotic datasets appear to be more similar among each other than eukaryotic datasets (Figure 3).
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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