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We also showed the capability of our method in prioritizing candidate genes for diseases whose genetic bases are completely unknown.
To examine the performance of our method in prioritizing candidate diseases for a fixed query gene, we perform the following leave-one-out cross-validation experiment.
To validate our method in prioritizing malaria genes, we performed a "leave-one-out" cross validation analysis and examined the ranks of a set of malaria genes extracted from literature.
We demonstrate the effectiveness of this method in prioritizing candidate genes through a series of cross-validation experiments, and we show the possibility of using this method to identify diseases with which a query gene may be associated.
We perform three large-scale leave-one-out cross-validation experiments to examine the performance of the proposed method in prioritizing genes that are known to be associated with certain diseases (i.e., disease genes) from a set of candidates.
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They conclude that random walks outperform other distance-based methods in prioritizing related disease genes, and similar approaches have been applied in genome-phenome networks [ 26, 27].
Note that uncertainty in the set of causal genes that we use as reference set will lead to an underestimate of the performance of our method in correctly prioritizing fine-mapped genes.
Feedback from the organization is that the method has been effective in prioritizing architectural concerns, that data collection and analysis is more extensive compared to traditional prioritization practices, but that extensive analysis seems inevitable in architecture improvement work.
Tiffin et al. [ 34] recently surveyed some of the methods for computational disease gene identification and concluded that using the methods in concert was more successful in prioritizing candidate genes for disease, than when each was used alone.
An understanding of learning preferences, although not the only factor to consider, can assist in prioritizing which pedagogical methods are most likely to be effective [ 16, 17].
We tested the method in large-scale protein-fold-recognition experiments and achieved significant improvements in prioritizing the best template structures.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com