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Many advances have been made in computational ADME modeling.
A lot of effort has been made in Computational Auditory Scene Analysis (CASA) to segregate target speech from monaural mixtures.
Building on initial work [ 74], recent strides have been made in computational techniques for meeting this challenge.
Although much progress has been made in computational approaches for B-cell epitope prediction, there still exist several aspects for further investigation.
On the other hand, although a great progress has been made in computational prediction of operons [ 7- 14] and small RNA genes [ 15- 18], the accuracy of these predictors is still low [ 13, 19], and they can only predict the static longest possible operons without considering possible alternative operons [ 7- 14].
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Significant progress has been made in the computational identification of putative CREs in a variety of species [3] [7].
Much progress has been made in developing computational methods that predict single locations for proteins.
Although some progress has been made in the computational prediction of FRET changes [ 4], empirical screening remains the most effective method of achieving substantial improvements.
While excellent progress has been made in using computational RNA structure prediction tools to engineer RNA gene regulatory mechanisms, we emphasize that their inherent approximations should be kept in mind when applying them.
In recent years, considerable progress has been made in this area using computational chemistry and carefully controlled experiments for isolating the chemical kinetics in the absence of transport effects.
Nevertheless, significant breakthroughs have been made in the development of computational methodologies which allow us to identify new driver mutations and genes by analyzing large sets of patients with different tumors [ 10].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com