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Functional genomics using high-throughput approaches provides more efficient metabolic engineering design and analysis, but still has limitations.
The combination of both approaches provides more information that help to improve the accuracy of the final gene models.
Which of the two modeling approaches provides more valid results is difficult to determine, but one factor may be the nature of the air pollutant variation in regards to the within- versus between-city/area.
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While these approaches provide more sophisticated optimization methods, their implementation in resource-constrained hardware platforms such as FPGAs is challenging.
It is illustrated how both approaches provide more meaningful comparison outcomes than the statistical t-test for differences.
Set theoretic approaches provide more interpretability because they can account for addition and subtraction of entropies.
These approaches provide more precise estimates for the outcomes of diabetics undergoing hemodialysis.
Assignment tests and similar approaches provide more direct inferences of dispersal and contemporary gene flow, avoiding a number of problems associated with traditional indirect inferences [ 6, 8].
However, when we consider the tagging behavior of biocurators, the co-occurrence-based approaches provide more opportunities to curate terms intuitively – that is, the co-occurrence-based approaches find more unknown patterns than those found by the network-based approaches.
Both approaches provide more accurate classifications than those based on BLAST, but they work for fewer sequences, as Pfam hits are less frequent than BLAST hits (typically ∼20% of the genes).
This result is rather counterintuitive, since three-, forr- or five-enzymes based approaches provide more information and should be able to better discriminate between false positive and false negative errors.
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