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In Figs. 6(3), (7), since Yu's method detected elliptic arcs from the different ellipse from that of the other methods, we computed the inlier and outlier selection ratios for the corresponding ellipse.
As described in Methods, we computed a continuous prognostic score (PS) based on the grouping defined in the training set.
For each of the mapping methods, we computed the average and standard deviation of several mapping statistics across the 57 samples.
Then using Benjamini-Hochberg's [ 49] and Storey [ 50] methods, we computed the false discovery rate (FDR) associated to the different p-values.
To assess the similarity between two methods, we computed the correlation coefficients (R) between the standardized coefficients generated from the two methods.
To evaluate the consistency between the six methods, we computed pairwise Spearman's correlations as well as intraclass correlation (ICC) for fold change values of all genes, along with the corresponding p-values.
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From the solution of the 3 methods we compute the density at time t = 100, see Figure 6 (left).
By combining molecular dynamics (MD), density functional theory (DFT), and Boltzmann transport equation (BTE) methods, we compute all of the transport coefficients without empirical parameters.
To measure the stability of the methods, we compute (i) the Spearman correlation between the two pairwise Euclidean distance matrices of the pairs of predicted structures and (ii) the RMSD between the rescaled predicted structures.
Similar to the group selection method, we computed a score for each gene in each iteration of the method to capture its effect on the correlation with the projection targets.
In the second method, we computed the odds ratios based solely on the weights assigned to observations (reciprocal of probability for SU and reciprocal of one minus the probability for GMW) rather than stratification by propensity scores.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com