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The reasons we choose it as a benchmark dataset for the current study are as follows.
The 270 associations (see Additional file 1) were used as the benchmark dataset for the performance evaluation of our model.
The lack of a public benchmark dataset for the task prevents the comprehensive comparison of the results with the previous work.
To study the computational performance, we used the benchmark dataset for the tyrosine-protein kinase ABL1 (PDB code 2HZI), containing 295 known active ligands and 10,885 inactive ones.
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Our performance studies show that the docking workflow for CADDSuite using the representative benchmark datasets for the protein kinase ABL1 with over ten thousand compounds scales almost linearly for up to 500 concurrent processes.
We first applied metaMix on a popular benchmark dataset, for which the community composition and the read assignment is known.
For this work, the cdc15 experiment was selected as the benchmarking dataset for the generation of YCCPN because it contains the largest number of data points (24 samples), thus providing the largest number of instances for the machine-learning method.
We produced a benchmark dataset for comparing the performance of PAUDA, BLASTX and RAPSearch2 by taking the first 600 000 good quality reads from each of the 12 samples published in Mackelprang et al. (2011).
The dataset is useful for building QSAR models for predicting activity against one or more specific targets for novel compounds and will also serve as a benchmark dataset for evaluating the performance of various machine-learning algorithms, especially multi-target learning algorithms.
We utilized these five sRNAs as a benchmark dataset for evaluating the results of our computational analyses of sRNA sequences.
Please note that 12 out of all 14 alleles are not in any of NielsenSet1, NielsenSet2 and NielsenSet3, which means that it is a good benchmark dataset for examining the performance of pan-specific models on novel alleles.
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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