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The performance of the present method is evaluated on two benchmark datasets consisting of five types of membrane proteins.
Finally, we prepared several benchmark datasets consisting of only representative drugs by varying the threshold from 0.1 to 1 by 0.1 increments on the hierarchical clustering tree and used them to examine the effect of the threshold on prediction performance.
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The assessment is made on a benchmark dataset consisting of almost four years of one minute resolution electric power consumption data collected from an individual residential customer.
On a benchmark dataset consisting of 52 membrane proteins composed of 80 chains with pairwise sequence identity <20% to avoid homology redundancy, the MemBrain-Rasa achieves a Pearson correlation coefficient of 0.733 and mean absolute error of 13.593, which are significantly enhanced compared to either the machine learning-based or template-based engines.
Only ATHLATES showed comparable performance on their benchmark dataset consisting of 11 samples.
We also present a comprehensive benchmark dataset consisting of RNA, exome and whole-genome sequencing data.
By collecting data in KEGG, a benchmark dataset consisting of 876 drugs, categorized into four target groups, was constructed.
In order to compare capabilities of the ant algorithm with REDUCE [ 11], a benchmark dataset consisting of 200 artificial genes was generated and numerical simulations were conducted.
To compare OptiType with ATHLATES, we used the publicly available subset of their benchmark dataset consisting of 11 samples from the 1000 Genomes Project (Liu et al., 2013).
A benchmark dataset consisting of 876 drugs was established by collecting data in KEGG, from which a training dataset and a test dataset were obtained by splitting the data.
Three different tools for protein clustering, ORTHOMCL [ 27], BLASTCLUST [ 28] and GENERAGE [ 29] were evaluated using a benchmark dataset consisting of experimentally validated gene families collected from the literature (Additional file 5).
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