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The significant overlap between our computational predictions and RNAi screening results suggests that RNAi screen results can be improved by using computational predictions to guide experiments, performing RNAi screens in size-reduced, prioritized subspaces predicted by our model, thus allowing more tissue types or experimental conditions to be tested with the same resources.
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For example, Ridder et al. [ 11] used computational predictions to generate an in silico library of biotransformation products resulting from human metabolism of polyphenols in tea, and found that only 23%% of the predicted products had entries in the Pubchem database.
We thus used computational prediction to identify candidates for further testing.
Therefore, performing the CvManGO analysis using computational predictions from multiple sources does not seem to significantly enrich for genes that can be updated, as compared to using computational predictions from a single source.
Here, we have taken the eQTL mapping paradigm to the single nucleotide level by predicting specific nucleotide differences that cause gene expression divergence using computational predictions of TFBSs.
However, determining protein-coding genes for most new genomes is almost completely performed by inference using computational predictions with significant documented error rates (> 15%).
Class I and II transposon sequences were initially identified by searching among the repetitive sequences for putative transposon elements using computational predictions based on BLASTX analysis.
Given the wealth of novel biodegradation pathways obtained using computational prediction methods, it is necessary to evaluate their relative feasibility.
Using computational prediction, potential targets for these miRNAs were identified, leading to the construction of an interaction network related to lactation.
Using computational prediction and in vivo validation we were able to identify 19 new photoreceptor-specific CREs, thus doubling the number of known photoreceptor-specific CREs.
Using computational prediction confirmed with in vitro and in vivo experiments, we report that the expression of versican 3'UTR not only antagonizes miR-199a-3p miR-199a-3p miR-199a-3psteady state expression.
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