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Again, bioinformatics databases were used to map between the obtained gene lists and interacting miRNAs and TFs.
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The methods are often working at the limits of signal to noise and are dependent on the information content of the bioinformatics databases being used for LM prediction [3], [25], [26].
Within each transcription pattern that we identified, bioinformatics databases are used to delineate which networks are involved.
Various predictive and comparative bioinformatics tools supported by biological databases were used to annotate putative open reading frames (ORFs) and other functional elements [ 21– 27].
BIND relies on Entrez GIs as the primary identifier for sequence interactors (protein, DNA, RNA), with Entrez gene IDs taking a secondary role, though many other references to bioinformatics databases (xrefs) were used.
A bioinformatic strategy based on mirSVR scoring in miRBase database was used to predict candidate miRNAs, targeting the HtrA1 transcript.
Bioinformatics tools were used for data evaluation.
To investigate what functional modules of zebrafish were induced by C. albicans infection, the bioinformatics database DAVID [ 27] and GO annotations were used for the analysis of zebrafish proteins.
An integrative approach combining results from selected web-based toxicogenomics and genomics databases as well as bioinformatics tools was used to prioritize candidate nsSNPs markers for arsenic responsiveness of protein targets.
Additionally, a bioinformatics approach was used to study the representation and variability of the identified epitopes of all database recorded HIV-1 clade B Gag T-cell epitopes.
Bioinformatics is used to analyze the available databases to determine whether a protein contains the peptide sequence or similar domain to the sequence selected.
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