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Although classification of the blood data was better in higher dose groups at the later time points as compared to the liver data, classifiers derived from it was not able to predict animals in some cases where the hepatotoxicant elicited a different phenotypic response with the animals of a particular dose\time group.
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The current study has shown that when there is a distant relationship between the animals in the test and reference data sets, gBLUP is still able to predict an animal's breeding value with some accuracy.
However, mosquito surveillance was able to predict cases in animals and humans.
It is important to be able to predict what the animal is about to do next, to anticipate what is about to happen and be prepared to film it.
Internal model validation (predictive performance) showed that the models were able to predict the count of the animal population on groups of farms that were located in randomly selected 3 km zones with a high level of accuracy.
In addition to discriminating between compounds that are hepatotoxic and ones that are not, their predictive models were in most cases also able to predict what kind of toxicant the animals had been exposed to a direct-acting one that causes damage itself, a cholestatic one that interferes with bile, or a steatotic one that drives buildup of fat in the liver.
Genomic data improved our ability to predict animal breeding values.
We conclude that although this type of protein-protein interaction prediction is highly dependent on the degree of conservation of protein-protein interactions between Arabidopsis and yeasts or animals, we were able to predict interactions with roles in diverse biological processes.
YLoc-HighRes (animal version) is able to predict a localization shift from the peroxisome to the mitochondrion.
Although gBLUP does not predict the breeding values for these unrelated individuals as accurately as Bayes B, it still relies on QTL information to better predict the relationships between animals, since it is able to predict a proportion of breeding value in both reference populations 2 and 3 under the IM, whereas under the polygenic model this accuracy was zero.
This is non predictable, although many people would like to be able to predict using genetic sequences or other information about organisms that are carried by animals, to date it's not been possible to predict when and where an outbreak will occur.
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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