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Currently, prediction of AKI with classical tools remains uncertain.
Currently, prediction of risk for complex diseases is based mainly on pedigree analysis but this approach yields predictions of risk that are of low precision; for example predictions would be identical for full siblings without offspring, yet the genetic variation among them accounts for half or more of the genetic variance [3], [4].
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Perception might be seen as the currently best prediction of expected input.
Currently, the prediction of retrocopies in entire sequenced genomes relies on the identifications of intronless duplications of multiexonic genes, known as parental genes.
Currently, predictions in regards to whether a residue is exposed or buried are used in a wide variety of protein structure prediction engines [6], [33].
Currently, an accurate prediction of the evolution of the tumor is one of the biggest challenges for clinical oncology.
The model does not currently enable differential prediction of changes at the protein-coding level and those at the gene expression level.
No means currently exists for prediction of mIBG uptake in tumour cells of individual patients other than semiquantitative inferences from diagnostic scanning which depend on the continued existence of a macroscopic tumour mass.
The same experiments and analyses are currently performed for the prediction of human specific toxicity using human liver slices.
Currently available datasets for prediction of the local solar resource in south Louisiana rely exclusively on modeled data by various schemes.
While one would expect computational approaches to be an integral part of such investigation, very few in silico methods are currently available for the prediction of natural substrates of proteases.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com