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With and already calculated, we next calculate.
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After reads mapping, transcripts assembling, and expression level calculating, we next sought to identify differentially expressed genes between samples with different treatment.
To assess the differential contribution of the various risk factors to the prediction of diabetes, we next calculated the ROC curves for WBC count and for other, well-validated diabetes risk factors.
We next calculated gain/loss statistics based on raw cell count data and based on tree edges, as we did with the CC data.
To quantitatively express the roundness of PSC colonies, we next calculated the aspect ratio of each colony from 3D ePD images.
We next calculated a TASA score, based on the 18-shared TASA genes, and found that it is highly enriched in NAT in most tissue types, with relatively low variation between NAT samples (Fig. 6b).
We next calculated the spatial similarity (Pearson correlation) of the MO activity patterns for each condition (i.e., ×2 or ×6) in each parcel to the corresponding GA IA pattern difference maps.
We next calculated the amino acid variability at each position in the aligned adenoviral fiber-knob sequences, which revealed regions of broad conservation corresponding to β-strands which make up the main fold of the fiber-knob trimer (Fig. 1d).
We next calculated the variance of the fixed effects across countries (given scaling model z).
We next calculated cross-correlations for the four short time periods previously defined (Fig. 10) as showing specific local or system noise pulses (Fig. 13).
We next calculated the Bayesian information criterion (BIC) and adjusted (R^2) values to compare the estimated additive models for each response.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com