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To compare model gene essentiality predictions with observed phenotypes, gene lists were compiled for genes considered essential, and those which have been observed to cause auxotrophies when deleted.
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The model predicted gene essentiality with an accuracy of 86.6% (Supplementary text ).
Secondly, we assessed the model against gene essentiality results on 9 defined environments.
The algorithms GapFind/GapFill [ 9] and GrowMatch [ 15] were later developed, and could predict missing reactions by connecting model gaps and by comparing model predictions to gene essentiality data, respectively.
In phase II we compared essentiality prediction for all 1445 genes involved in the model with experimental gene essentiality datasets; its error rate in predicting gene-knockout phenotypes decreased by 46% over the best previous model.
Validation of metabolic models by predicting gene essentiality is a routine procedure and the frequency of true positives is a measure of the network's quality.
The development of the modified in vivo network iNJ661v was ultimately based on the growth of the bacterium under different in vitro conditions (iNJ661) and modifications to better model experimental in vivo gene essentiality.
As a demonstration of the utility of our model in kidney disease research, we took DKD as an example and performed gene essentiality and flux variability analysis for DEG to characterize the gene deletion phenotypes.
However, our non-tissue specific model was unable to predict gene essentiality for many of the metabolic genes shown to be essential in vivo.
Researchers have previously used different data sets to define gene essentiality for model analysis [ 9, 12].
In order to evaluate the model's capability of predicting gene essentiality we performed simulations of growth of single and double gene knock-outs.
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