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Here we developed a comprehensive disulfide bond prediction algorithm that not just increased the success rate of predictions but also improved the quality of crystallized target proteins.
For documents with greater than one prediction, GNAT recall is higher than precision, consistent with the higher rate of predictions per article by GNAT relative to text2genome.
Over the years, IT-based splicing predictions have made progress and the overall rate of predictions concordant with experimental validations is around 88%.
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Moreover, the less training samples also caused the fluctuation of low correct rate of prediction.
We can see that the correct rate of prediction is about 50% with small fluctuations.
The rate of prediction accuracy was thus at a high level of 72.7%.
The experimental results showed that the matrix size of original vowel voices signal could affect the correct rate of prediction.
It could be seen that the mean value of correct rate of prediction was about 50% with small fluctuations.
Meanwhile, we repute that the low correct rate of prediction and small fluctuations were caused by the few training samples.
Therefore, the correct rate of prediction for compressed data may not be the same under different random measurement matrix.
Also, the correct rate of prediction was stable under different random measurement matrix and different compressed ratio.
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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