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To minimize sample set bias and to aid in the assessment of intermediate models, we employed one-third "out-of-bag" (OOB) error estimation and an external 100-fold bootstrap validation with 10% holdout bootstraps.
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From samples with >100 individuals available (Table 1), a sub-set of 100 individuals was chosen at random for subsequent analyses in order to minimize sample size effects.
To minimize sample variation, we used high quality RNA.
Store RNA solutions on ice to minimize sample degradation.
We have previously established that data derived from sets of six or more synovial tissue fragments minimize sampling error [ 19] and pools of greater than six fragments were used for qPCR analysis.
To minimize sampling variability, the following steps were taken.
An age cohort gene signature was obtained by training PAM software [ 46] on RNA sample set 2 (UCSF), minimizing the cross-validation error for the individual age cohorts.
Donor control, in addition to normal adjacent to tumors, precancerous lesions, and tumor samples will provide the best sample set for resolution of genetic alterations that are relevant to the disease process by minimizing the potential implications of field cancerization.
We generated 3 sample sets each, derived from Cy3 labeled Smad4 re-expressing cell lysates and Cy5 labeled Smad4 negative cell lysates, whereas the second sample set (again three lysates, each for Smad4 re-expressing and negative cells) was labeled vice versa to minimize the identification of false positive protein spots.
To minimize variation from different cardiomyocyte preparations, equal amounts of RNA from three separate myocyte preparations were pooled to generate a single sample set and three such sets were hybridized to separate microarrays.
Based on a sample set requiring 79 CFD simulations, a global utopia point is found that minimizes both objectives.
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