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In the ClaNC results, both cell lines HCC-95 and HCC-15 were classified as classical samples.
The Classial-specific region (B) in Figure 6 includes six contextual gene sets, where one is over-expressed and the other five are under-expressed across Classical samples.
We then compared the cattle BSE control with the 701 classical samples available for further analyses (648 sheep and 53 goats).
When the accuracy of prediction is broken down by class, however, the phosphatase-only gene set accurately predicts the sample class for 74±7% of the classical samples compared to 73±5% using the whole genome.
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The classical sample size formula (CLASSIC) for comparing normally distributed endpoints between two groups has shown to be inadequate in this setting (underestimated study sizes).
Kluvánek [54] was the first to place sampling theory in the LCA group setting by establishing the abstract analogue of the classical sampling theorem (i. e., the exact sampling formula (1.7)).
Unfortunately, this is beyond the scope of classical sampling theory.
The classical sampling theorem states that x ( t ) = ∑ i ∈ Z x i 2 B · sinc ( 2 Bt − i ) (27).
However in our article [14] it was shown that the approximate sampling theorem, the ASF, is equivalent to the classical sampling theorem CSF.
The computations show that, as compared to the classical sampling expansion in [14], the variant with the Gaussian multiplier provides a strikingly high improvement of the accuracy.
Unlike the classical sampling theorem [78] based on the Fourier transform, the signals are assumed to be sparse in an arbitrary transform domain.
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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