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The quality of seed clique set Seed i, generated from the attributed PPI networks constructed using T i type PPI data, indicated the value of T i type PPI data for protein complex prediction.
(5) C _ D e g r e e (T i ) = ∑ ((C j, E c j ), S j ) ∈ S e e d T i C l i q u e _ S c o r e ((C j, E c j ), S j ) ⋅ | E c j | E T i (6) w i = C _ D e g r e e (T i ) ∑ i = 1 2 C _ D e g r e e (T i ) where Seed Ti denotes the seed clique set generated from the attributed PPI networks constructed using T i type PPIs, and | E Ti| is the total number of T i type PPI data.
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We can use t i = I, and thus, we have ( Li = frac{s(i)}{s(i - 1)} ) which shows the percent of change during a day.
These results were obtained from the first PC for an EA analysis (since it indicated the strongest separation) using T d i f f i 1 E A only to determine differential gene contribution.
We obtained t F I T R using the statistical package R (http://www.R-project.org) with 100,000 simulations of t F I T R for each combination of R and L. The test using t F I T R (i ) is the FITR, an exact significance test assuming Y h,i follows the standard normal distribution.
Thus, we consider only linear dependences in our fitting procedure using T = αI and τ = βI.
The discriminatory power of the different CTX isoforms or CTX ratios in distinguishing groups from controls was assessed using T or Z scores (i.e. number of SDs from the mean of the controls).
As characteristic time scale we use T = 1/δF, i.e. t = Tτ.
I use T-Mobile, though I can leave anytime I want.
We used T value of 50, i.e., a differentially expressed regulation was required to be found and to be identical in at least 50% of the sample-specific regulation networks in order to be classified as a core regulation.
Throughout this work, we will use u t (i) to refer to the ith element of the vector (vec {u}_{t}).
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CEO of Professional Science Editing for Scientists @ prosciediting.com