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1The authors are grateful to Ke Hu and Deliang Wang for the assistance in applying their algorithm [38] to the reported dataset.
The reported dataset was obtained from homogenization of 145 hearts (∼20 µg protein) and further processing with a gel-LC-MS/MS proteomics approach.
All hits to the reversed H. salinarum protein sequences were also rejected and the false positive rate of the reported dataset at the peptide level is estimated to be 1%.
Some of the trials involved three or four treatment arms, but to enable application to BRMA here we only consider outcome differences between the control arm and the first treatment arm in the reported dataset [ 7].
This table also describes the version of the assembly and annotation that is represented in the reported dataset and additional details on the protein-coding annotation results, statistical metrics that are commonly used to evaluate the quality of the assembly, and one measure (EST count) of the amount of same-species transcript data that was available for that annotation run.
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The robustness and accuracy of the QSAR models were confirmed by the satisfactory statistical parameters for the experimentally reported dataset (R2p = 0.853, Q2LOO = 0.785, R2L20%O = 0.742 and r2m = 0.715) and low standard error values (RMSEp = 0.208, RMSELOO = 0.321 and RMSEL20%O = 0.376).
The QSAR model developed showed satisfactory statistical parameters for the experimentally reported dataset (r2 = 0.9415, q2 = 0.8958, pred_r2 = 0.8894 and F-test = 112.7314), thus judging the robustness of the model.
To further enrich the variability in clinical characteristics at delivery compared to the earlier reported dataset [ 16], an additional cohort of women undergoing preterm caesarean delivery was recruited.
The miRNA expression profiling is, to our knowledge, the first such reported dataset on the HapMap samples.
We designed a custom array gene chip that included approximately 650 top performing candidate genes identified by applying the supervised principal component survival analysis in each of the four previously reported datasets.
With this in mind, we want to find the applicability of these socio-economic factors towards improving the predictive power of the crime reports dataset.
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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