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As obesity is often associated with a number of health problems, the presence of comorbidities in the Primary Care dataset were identified using the Charlson Index.
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Importantly, these transcription factor binding site datasets were identified using the mincing strategy and therefore allow a direct comparison with the pulverization method.
Potential outlier datasets were identified using robust Mahalanobis distance squared values associated with the peptide abundance vector (rMd-PAV) and a p-value threshold less than 0.001 as recommended by the developers of the algorithm [ 22].
Genomic binding sites in the ChIP-Seq datasets were identified using the peak calling algorithm MACS (version 1.4.0 beta) with default settings (band width = 300, model fold = 10, 30, p-value cutoff = 1.00e-05, range for calculating regional lambda = 1000 and 10000 bps) [ 62].
In the Tokyo dataset, SLNs were identified using a radioactive tracer (99mTc-phytate).
In the Seoul dataset, SLNs were identified using both blue dye and a radioactive tracer.
In the Kyoto dataset, they were identified using blue dye and a fluorescence navigation technique using indocyanine green.
For the full E14.5 liver datasets, differentially methylated regions were identified using the R package bsseq [ 30].
Periodically expressed targets that showed statistically significant differences in expression (false discovery rate q-value ≤0.05) in the normalized datasets of 36 summer samples were identified using the GeneCycle R package [ 39].
Detail information of the maximum flow of each candidate protein is also provided as an Additional file 2. In this prioritized list, proteins which interact with the host were identified using a dataset retrieved from a computational prediction of Homo Sapiens- Mycobacterium tuberculosis H37Rv protein-protein interactions [37].
Sixty-nine significant pathways were identified using the PLIER dataset and eight significant pathways were identified using the RMA dataset.
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