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In-depth explorations of inference methods often rely on simulation studies, which are commonly used to investigate the performance of species tree inference methods on simulated multilocus datasets [ 10, 28- 32].
We show the performance of those methods on simulated data.
The new method is compared with a variety of existing Bayesian and non-Bayesian methods on simulated and real datasets.
We evaluated the two methods on simulated data and a data set of 112 human kinases assembled from the public database ChEMBL.
We evaluated the methods on simulated data sets, a data set with affinity data against a large fraction of the human kinome, and four smaller subsets of the aforementioned kinome data.
We compared the running time of all supertree methods on simulated data.
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Section 4.2.1 describes the development experiment, while Section 4.2.2 evaluates our proposed method on simulated data.
By testing the method on simulated datasets we can understand its performance features and liabilities.
They show the performance of their method on simulated data and provide a complete analysis on single unit activity recorded on a monkey during a sensory motor task.
We evaluated the method on simulated and in vivo human brain data, comparing the results with the traditional single-tensor and with a probabilistic tractography technique.
This paper presents the first experimental results obtained by applying the proposed method on simulated signals in different environments in order to evaluate its performances and application range.
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