1
We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out.
2
We also evaluate our approach on the basis of simulated data.
3
Simulated data were used to confirm the veracity of the model.
4
We evaluate the methods based on both simulated data and real RNA-seq data.
5
To validate our proposed technique, we conduct experiments using real and simulated data.
6
We also evaluated the utility of profiles created based on simulated data sets.
7
The methodology is illustrated and evaluated using both real and simulated data sets.
8
Illustrations are given using simulated data and twin data relating to blood pressure.
9
Simulated data are used in order to test the methods.
10
The method also returned accurate estimates of C and theta using simulated data sets.
11
Performance on a standard set of simulated data is indispensable for recognising optimal methods.
12
The programs' performance has been tested and validated on real and simulated data sets.
13
We applied national and international guidelines for viral load monitoring to the simulated data.
14
We compared our proposed methods, the single-locus method, and a sliding window method using simulated data.
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The close agreement between measured and simulated data confirms the underlying mechanisms introduced in the model.
16
The crystallographic restrained-parameter least-squares refinement program PROLSQ is used to refine models against these simulated data.