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In each test run, the method selects noisy readouts from the TOR models.
Then, we add 10 5 iterations to this limit and run the method until the desired 5 min are reached.
Because the exact method requires O(n2 n +3) compute time, it was only feasible to run the method on the low-divergence data set, where n = 16.
I would still like to see the method tested on a larger set of sequence data from "real life": e.g. take a set of (aligned) ribosomal RNA sequences, e.g. 50 from the bacteria, and run the method: how many positions are useful?
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The parallell implementation was not strictly necessary, but made it much more feasible to run the methods on all the relatively large datasets.
We hide the associations in one group and run the methods on the remaining associations, repeating three times to ensure that each group is hidden exactly once.
The absence of non-volatile components in the mobile phase allows running the method with evaporative light scattering and MS-detectors.
In Table 2, we show the results of running the method 10 times and order the relevant genes by decreasing number of hits.
We then ran the method of Braga et al. (2008) on the simulated sequences (comparing each time the initial sequence and one sequence with simulated inversions).
In Table 2, we show the results gathered from running the method 10 times and order the biologically relevant genes by decreasing of number of hits.
We ran the method of Magwene et al. with a default genetic window size of 30 cM, as recommended by the authors [ 7].
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