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We showed previously that the expected rate of vulture population decline estimated by our method from surveys of diclofenac in ungulate carcasses was higher, though not significantly so, than that observed using repeated counts of vultures during the same period [7].
The likelihood of the selection coefficient is estimated by our method from 1 million iterations of the importance sampling algorithm.
We have shown that the quality of H3K4me3 profile acquired by our method from 1 000 cells is comparable to that of traditional approach using bulk materials.
Unlike the transient and inter-complex binary interactions discovered by Y2H, the binary interactions identified by our method from AP-MS data are enriched with stable and intra-complex interactions.
The high recall in both ontology cases indicates that a large number of GO terms is recovered by our method from all of the GO terms that are relevant to the search.
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Moreover, the tissue-specific patterns of gene expression that are detected by our method arise from a limited number of genes, rather than as a general consequence of LTR integration.
The predicted associations by our method could be from the result of sharing the same disease genes for two different diseases.
The improvement obtained by our method is evident from the percent increase in F1 that we achieve over the other imputation methods: 18.4% over deletion, 10.4% over mean-value and 46.9% over RF imputation.
Here we compare the steady states reported by our method with the ones from the methods of Li et al. and Davidich et al. For this we use vector notation to represent the activity levels of an ordered gene set.
A neighbor-joining phylogenetic tree was also generated for the RT domains identified by our method in LTR retroelements from D. melanogaster and D. pseudoobscura.
First we compare the accuracy of the ancestral genome reconstructed by our method with the result from both Ma's maximum likelihood approach and the pure ASMedian solver.
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