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In order to correlate the gene expression data with the secretome, the fold change from cellulose versus glucose in both datasets was compared.
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The majority of the analysis was carried out using the clustered datasets and lipid-free metabolite dataset, except for preliminary analysis, where these randomly selected molecules were used and in the case of Ro5 test, where both datasets were compared.
If a gene had zero or low expression in both datasets being compared, such as in correlation and differential expression analysis, that gene was omitted from that comparison.
The SSU processome protein coverage of the aforementioned datasets was compared to coverage from literature-curated (LC) sources [37] [51].
Later-life hypertensive status between the two datasets was compared.
Both PET datasets were compared visually with a focus on tumour delineation, the conspicuity of lymph node metastases and artefacts.
Using a similar analysis approach to Sridhar, both the SmvsNS and the BuccalCompare datasets were compared against six gene lists in a GSEA enrichment analysis.
The performance of the proposed algorithm on both BrainWeb and IBSR datasets is compared with some reported fuzzy approaches: the standard FCM algorithm and the FCM algorithm with incorporated neighborhood information (NFCM [12].
Analyses using both the full and rarefied datasets were compared and, as the results did not differ, usually only those from the rarefied data are reported here.
Both unassembled and assembled EST datasets were compared, at the nucleotide level using BLASTn (e-value < 1e -05), with genomic sequence data publicly available for H. contortus (http://www.sanger.ac.uk; 21st August 2008).
The calibrated datasets are compared to ground-based observations, showing an improvement for more than 65% of the sites tested.
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