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We introduce here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating data in this manner.
Last, after simulating data in a similar manner as was done for data-set 1, we plotted the real data against the simulated data for each group separately.
Simulating data in this manner is important because the genomic context of genetic risk factors is becoming increasingly apparent as we analyze and interpret polymorphisms identified through genome-wide association studies [ENCODE Project Consortium et al., 2012; Gerstein et al., 2012; Karczewski et al., 2013].
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In order to compare the results of experimented data and simulated data, in our simulation, all the parameters are the same with the experiment parameters (including the filter parameters).
We evaluate the performance of our proposed weighted quantile normalization method using simulated data in sections " Simulation data sets" through " Normalization accounting for unrelated covariates".
This distribution is of course different from the ones of the simulated data in Figures 1 and 2. However, simulations will confirm that it is a reasonable choice.
Methods designed to simulate experimental data are constantly in development8, and numerous attempts have been made to improve the quality of simulated data (e.g., incorporating real and simulated data in one comprehensive, semi-real data set).
This is validated by simulated data, in the case of a resonant clamped-clamped beam.
They also confirmed the simulated data in both equilibrium height and the time trajectory.
Numerically simulated data in this study were also used to validate the proposed model.
We provide a thorough evaluation of the proposed methods on simulated data, in terms of membership and blockmodel estima- tion, predictions out-of-sample and run-time.
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