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To address this question, we first mapped miRNA target genes from mRNA expression, mRNA decay rate, and protein stability index datasets to each other.
The number of luminal groups, however, was reduced from three to two: luminal A and luminal B. Using centroids derived from the five groups, they classified samples in two independent datasets to each of the groups.
Metadata or "data describing the data" provides the information necessary for relating datasets to each other, to enable the respective dataset to be positioned within a particular research project (Rocca-Serra et al. 2010).
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To determine the similarity of each dataset to each other, total nucleic acids between each database and total partial ORFs (pORFs) between each database were compared to provide an indication of the number of homologous sequences shared between each pair of datasets (Table S3).
Histogram at top shows the fractional contribution of particles from each dataset to each Group.
Then the proposed approach is applied on the volumes of the datasets to segment each into the five classes (WM, GM, CSF, BG, and tumor).
We created three datasets to build predictors for each of the non-crystallizable classes (MF, PF and CF).
Hence, we reduced CisFinder's motif datasets to ≤ 100 motifs in each dataset for STAMP to process.
We also produced datasets similar to each of the three datasets described above with paired-end reads with an insert size of 2 kb, to evaluate the performance of paired-ended assembly.
Citation rates and altmetric scores were each standardized by dividing each metric by the highest in the dataset to give each paper a normalized metric score between 0 and 1, which was summed to create an aggregate impact score.
Therefore, this study suggested an integrated POE, that combined a quantifiable environmental dataset to indicate each individual occupant's satisfaction with each IEQ element.
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