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The Scaffold Tree for each dataset was generated by using the linear fragmentation function in the Molecular Operating Environment MOEE) suite [29].
A CNS PET tracer dataset was generated by compilation of 31 PET tracers that either have been evaluated in house at the PET centre at Karolinska Institutet, Sweden, or are related to targets that have been examined at the PET centre.
However, the HGDP dataset was generated by Human-550 chips of Illumina and required further data processing before use.
The control dataset was generated by performing several pull downs in cells that expressed the vector alone to identify proteins that non-specifically bind to the affinity beads used in the purification.
The PFOLD grammar in particular performed relatively weakly when the null dataset was generated by gsimulator (which has low GC content and a low degree of conservation), but was the strongest when using a simgenome-generated dataset (wherein the GC content is closer to uniform and the substitution rate more heterogeneous, thanks to conserved information-rich regions such as exons).
The primary dataset was generated by DJ in the lab of LL.
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The synthetic dataset is generated by LinkBench and the graph database benchmark published by Facebook [1].
Since the synthetic dataset is generated by a random function, the data generated each time is inconsistent.
The training dataset is generated by selecting features, or samples, that exhibit data dependency to the keys.
The wind speeds in the dataset are generated by MASS v.6.8 mesoscale model, which is initialized with input from Atmospheric Research Global Reanalysis and assimilates both surface and rawindsonde data.
In the proposed approach, dataset is generated by own because there is no standard database that is available for IVS of different singers.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com