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We evaluated the implementation and accuracy of this proposed methodology by analyzing a simulated recombinant population generated from known sequence data.
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The method has been illustrated by analyzing numerically simulated signals.
Values to estimate the efficiency and resolution of the design obtained by analyzing the simulated signals are presented.
By analyzing both simulated and real experimental data, we found that the MIM measure can be used to detect sequence specificity independent of presence of transcription factor (TF) binding motifs.
By analyzing the simulated voltage and current waveforms, power losses are evaluated with different converter operation points in [12], where the detailed electro-magnetic simulation model of MMC was applied.
We have compared the CHAMBER method with our previously published results analyzed by logistic regression [30] and by analyzing our simulated data by classification and regression trees (CART).
The false positive rate of imprinting QTL discovery by the model was examined by analyzing the simulated data that contain no imprinting QTL.
The algorithm was thoroughly tested by analyzing the simulated data by the FCS, FIDA, and BIFL 9, 16, 45, 64 methods.
LOCate is applicable to linkage analysis for ordinal or nominal traits, a versatility which we demonstrate by analyzing simulated data with a nominal trait, on which LOCate outperforms LOT, an existing method which is designed for ordinal traits.
The method is illustrated and validated by analyzing simulated and experimental signals of a planetary gearbox in a wind turbine test rig under nonstationary running conditions.
By analyzing data sets simulated with no population structure (i.e. derived from a single panmictic population) we estimated the probability of Type I error of their procedure to be 0.125%.
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