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Embedding is done in such a way that the positioning of the vectors in the space reflects the relationships between the original sequences as best as possible.
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Figure 10 Visual comparison between the original sequence, the non-parametric technique NPM2 and our method.
The Z-score is precisely the difference, in number of standard deviations, between the original sequence and the mean MFE of the randomized sequences.
After getting the scaffold we calculate the edit distance between the original sequence and the resulting scaffold.
We use this method as a measure of success in identifying the largest distance between the original sequence and the mutated sequence.
Figure 5 shows the averaged distance between the original sequence and the contaminated sequences when noise ratio increases from 0.01 to 0.5.
As the sequences are very long, it is infeasible to calculate the edit distance between the original sequence and resulting scaffold.
The system is based on both histograms and geometry as the core comparing mechanism between the original sequence secondary structure and all the possible point mutations' folding variants.
Given a noise ratio, we first generate 100 contaminated sequences, then calculate the distance between the original sequence and each of the 100 contaminated sequences, and finally we evaluate the average distance over the 100 tests.
The results showed that the product sizes of two markers (HAU-SNP304 and HAU-SNP504) were not different between Emian22, 3 79 and the original sequences, but those of marker HAU-SNP248 were slightly different between them.
The homology between these constructs and the original sequences is shown in Table 1.
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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