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Accordingly, on every group of benchmark data, we tested FFP, CVTree and UA with different parameter values and used those which produced the best results on this group.
To obtain simulated protein benchmark data, we generated a set of 125 protein sequences with a length of 300 aa each.
Using the HPO benchmark data, we quantify the accuracy of the prediction by comparing the predicted gene list of symptoms with that of the benchmark data.
In addition to the benchmark data, we use an artificially designed theophylline sensing riboswitch to compare the three SHAPE conversion methods with a prediction that directly includes ligand binding free energy of the aptamer (see Supplementary Material S5).
We set the default value for the depth at 3. As the benchmark data, we use the set of human promoter sequences in the cisRED database (Human v9.0, Robertson et al., 2006).
For each of the five groups of benchmark data, we used the word length k for which the k-mer approach produced the best results, i.e. trees with minimal average Robinson – Foulds (RF) distances to the reference trees.
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Using a large number of simulations on benchmark data sets, we show that the proposed CIR significantly improves classification accuracy without compromising interpretability of the fuzzy classifier.
As benchmark data set, we chose the publicly available ChemDB random background data set published in the virtual screening study by Nasr et al. [28].
With our clustering benchmark data sets, we compare the results to the known ground truth and observe that random swap finds the correct cluster allocation every time.
By performing the leave-one-out experiment of all 42 alleles included in the benchmark data set, we can validate the performance of the NetMHCpan method on 42 alleles with uncharacterized binding specificity.
Using six popular benchmark data sets, we compared RFA with other gene selection methods.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com