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For each sample both paired ends for both lanes were run together, effectively combining the multiple files per sample into one output.
Considering each resistance mutation and gene in turn, our prediction algorithm first genotypes a sample into one of three categories: clonal susceptible, minor frequency-resistant allele, or major frequency-resistant allele.
To bin expression data for GMIT network inference, we computed quantiles for each gene and classified its expression in each sample into one of three bins.
Total RNA of each sample was isolated individually, and then pooled with an equal amount from each sample into one for profiling.
In addition, we classified each sample into one of three categories according to the intensity: 1+, weak; 2+, moderate; and 3+, strong.
Using Samtools (Li et al. 2009), we merged read data from separate sequencing runs of the same sample into one single BAM file per individual.
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In spite of the fact that opinion leaders who may have a high influence on the other participants were purposefully sampled into one group and the moderator (MM) encouraged all participants to take equal part in the process, some related bias in the outcome cannot be ruled out.
On the other hand, LDA completely spreads the data samples into one global undiscriminative distribution of data samples.
Solid-phase microextraction (SPME) consolidates sampling and sample preparation into one step.
This decomposition maps 600,000 input samples into one sample per thread across the 2D grid dimension.
The resulting phylogenetic analyses of three types of cancer (ovarian, prostate, and pancreatic) that included samples from non-cancerous individuals grouped all cancerous samples into one group, at the bottom was a healthy group or groups and in between are what Abu-Asb and colleagues call a transitional zone.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com