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Just when the sun was setting, we made up some symmetrical looking weird things.
In this setting, we made it challenging in which not all the poses in the testing sets were available for training, e.g., θ train = {2, 3, 4, 6, 8} and θ test = {2, 4, 6, 7, 9}. 3.
For each parameter setting we made 100 replicate data sets, giving 6400 sets of partial trees for each of the ten hybridization networks.
To study breast cancer risk in an in vitro, animal and human setting, we made a distinction between tumour initiation and progression as most in vivo and in vitro studies can only address tumour progression.
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For each setting, we make comparisons between two different node models and between OR and UR.
Considering an idealized setting, we make use of the Eulerian (spatial) kinematic description for the belt, which moves quasistatically along a given contour.
In creating the patchy target setting, we make sure that the patches are sparsely placed while each patch has higher target density.
For approximately half of the compounds in the blind set we made read-across.
As Green Velvet was beginning to finish up his set, we made our way over to the main stage for the legend that is Eric Prydz.
Since equivalent transcripts would occur in the different cis-NAT data sets we made a non-redundant list, to analyse the total population of cis-NATs.
To better understand which functions are enriched in these gene sets, we made use of Gene Ontology (GO) term annotations from WormBase [ 36].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com