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We therefore suggest performing a grid search on a subset of the samples and selecting the smallest possible parameters that give a non-trivial clustering on every chromosome.
By improving RNA yields from small cartilage samples and selecting highly sensitive methods for quantitative gene expression analysis, we studied alterations in chondrocyte metabolism side by side with the histological appearance of cartilage degeneration in adjacent tissue.
We have demonstrated an efficient and economical way to develop SNP markers for QTL fine-mapping and provided a guideline for methods used in preparing complexity-reduced genomic DNA sequencing samples and selecting parameters for sequence data filtering and SNP prediction.
For each phenotype a set up-regulated and a separate set of downregulated genes was identified by comparing samples from that phenotype with all other samples and selecting genes that were differentially expressed at a false discovery rate (FDR) cutoff of 0.01.
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We detected genes significantly differentially expressed across all samples, and selected them for further analysis.
Sort the relevancy values of all samples, and select some top ranked samples as the final result.
At the end of the recovery period, individual blood samples and selected organs for histopathological examination were taken.
Involved pathologist carefully reviewed all CRC samples and selected tumor sections which were most representative of each tumor.
We then sort the relevancy values of all unknown samples and select the top ranked samples as retrieved samples, which constitute our predicted miRNA precursors.
A clustered heatmap of samples and selected genes was generated using the RMA expression values, uncentered Pearson correlation as a similarity measure and average linkage.
(c) Multidimensional scaling plot: the study samples and selected markers in the final classification model with the highest testing accuracy are presented in a multidimensional scaling plot.
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