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Applications of successful in silico target identification attempts are discussed in detail, which were based partly or in whole on computational target predictions in the first instance.
Computational target prediction combined with a comprehensive functional validation enabled the discovery of a set of twenty putative target genes for all productivity enhancing miR-30 family members.
We developed Chiminey under directions by quantum physicists and molecular biologists, to ease the steep learning curve in data management and software platforms, required for the complex computational target systems.
Using multiple computational target predicting algorithms together with the luciferase-based reporting platform, the interactions between HF-related microRNAs and the 3′ untranslated regions (3′UTRs) of neurohormone associated genes were examined and compared.
Such caveats are responsible for the increased interest in novel computational target deconvolution strategies for drug discovery [11].
Currently, miRNA target identification is based primarily on computational target predication algorithms.
While computational target prediction algorithms provide large lists of proposed miRNA targets, a relatively limited number have been validated.
Fourthly, we performed both miRNA-mRNA and miRNA-protein correlation analyses and integrated these with computational target predictions to study potential direct targets of miRNAs.
The ultimate goal of computational target gene prediction is to obtain a high quality set of candidate targets by scanning one or more genomes.
As part of the analysis, we combined temporal correlation analysis and computational target mapping to study the potential direct targets of miRNAs.
Furthermore, we developed a novel approach to systematically predict potential direct targets of miRNAs from the data through both miRNA-mRNA and miRNA-protein correlation analysis and computational target mapping.
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