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The results from the real data suggest that GCV-spline determines appropriate amounts of smoothing.
Both simulated and real data suggest that complementary epistasis contributes greatly to quantitative trait variation, and obscures the phenotypic effects of many 'downstream' loci in pathways.
Simulations and results on real data suggest that breeding values can be predicted with high accuracy using genetic markers alone.
Evaluations on both simulated and real data suggest that BWA is ∼10 20× faster than MAQ, while achieving similar accuracy.
Our results on both simulated and real data suggest that ARG-walker is a promising new method for estimating the individual recombination variations.
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The difference between the synthetic scores of the classical PCA method and the scores achieved using real data suggests that the bias in the sample eigenvalues is not the only problem in face data though.
The departure of the simulation from the real data suggests that the effect of technical noise on the test results would be limited.
While our analysis of real data suggests that this impact may be deemed reasonably moderate in some situations, the main concern still remains because the estimates employed are not sufficiently stable and the underlying model may still need further refinements.
The average node degree (in- plus out-degree) in the union-network generated from the model was 4.4, compared to 3.3 for the real data, suggesting that this is the case.
However, the analysis of both simulated and real data suggests that the false-discovery rates of prioritized lists may be still be large, even within the top portion of the prioritized lists.
The experiments on synthetic and real hyperspectral data suggest that the proposed algorithm outperforms the classical sparsity-based detection algorithms, such as Simultaneous Orthogonal Matching Pursuit (SOMP) and Simultaneous Subspace Pursuit (SSP) and conventional ℓ1 minimization.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com