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By setting different α, we determined the different weight of each aspect.
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To study the effect of parameter α on performance of ION, we evaluate the prediction accuracy by setting different values of α, ranging from 0 to 0.99.
By setting different values of α, we can control the diffusivity of the stochastic processes (({L^{1}_{t}})_{t in mathbb {N}}) and (({L^{2}_{t}})_{t in mathbb {N}}).
Column 2 to 11 represents prediction accuracy of ION in each top percentage of ranked proteins by setting different values of α, ranging from 0 to 0.99.
By setting different nonlinear coefficients, different models can be simulated.
By setting parameter α to value α=2, it reduces to η−μ distribution.
Genome-wide binding analysis of PML RAR α (Martens et al, 2010) revealed that various enzymes that can set different chromatin modifications are targeted by PML RAR α, including JMJD3 (H3K27me3 demethylation), SETDB1, JMJD1A (H3K9 modifiers), deacetylases like HDAC4 and 9, and genes involved in DNA methylation, such as DNMT3A (Table 1).
The semi-classical equations are obtained by setting β = α ∗ in the drift term and ignoring the diffusion term, and are thus d α d t = − ( γ + i Δ + i χ | α | 2 ) α − i ϵ − i κ α ∗, (10).
Reducing the HPF component by setting the factor α as less than 1.0 as in Table 1 significantly improves the recognition accuracy, regardless of the different structures and types of WS-HEQ.
This section will consider the simplified system that is obtained by setting α = 0.
By setting the parameters differently this improvement might be different.
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