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We benchmark the method by illustrating it on a simple synthetic experiment.
To benchmark the method, we developed a new gold standard corpus of manually annotated EOL taxon pages.
In order to benchmark the method, we extracted from the OMIM web resource [ 33] a list of genetic diseases that have been associated to two or more genes.
We integrate our approach into the rxncon framework to allow automatic model generation, and benchmark the method with the previously mapped MAP kinase network in yeast.
It is helpful to consider a simple toy system to illustrate and benchmark the method, in detail consider a 4-letter alphabet of equal weights and sequence lengths L = M = 10, 20.
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In six out of seven benchmarks, the method yields precise timings.
In prior benchmarking, the method has yielded affect intensity mean percentage errors of 5% or less on similar Dark Web forums [36].
We benchmarked the method against several existing techniques using numerical examples and found that it compared favorably in terms of solution quality and computational efficiency.
We benchmarked the method on the gene expression data of soybean.
In the proof-of-principle experiments, the authors benchmarked the method against 210 PDZ-peptide interactions of known affinities.
We benchmarked the method using simulated density maps of 11 protein assemblies at 20 Å, and an experimental cryoEM map at 23.5 Å resolution.
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