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step decrease » sizes decrease (Expand Search), we decrease (Expand Search)
teer decrease » greater decrease (Expand Search)
a decrease » _ decrease (Expand Search), _ decreased (Expand Search), _ decreases (Expand Search)
a step » _ step (Expand Search)
step decrease » sizes decrease (Expand Search), we decrease (Expand Search)
teer decrease » greater decrease (Expand Search)
a decrease » _ decrease (Expand Search), _ decreased (Expand Search), _ decreases (Expand Search)
a step » _ step (Expand Search)
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6901
Amiselimod (MT-1303), a novel sphingosine 1-phosphate receptor-1 functional antagonist, inhibits progress of chronic colitis induced by transfer of CD4<sup>+</sup>CD45RB<sup>high</...
Published 2019“…MT-1303 phosphate (MT-1303-P), an active metabolite of MT-1303, exhibits S1P<sub>1</sub> receptor agonism at a lower EC<sub>50</sub> value than other S1P<sub>1</sub> receptor modulators currently being developed. …”
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6902
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6903
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6904
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6905
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6906
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6907
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6908
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6909
Detail of the personalized-enhanced GCN.
Published 2025“…The aPFGCN module effectively reduces the dimensionality of features and decreases model complexity to obtain the final node feature representation by personalizing the adjustment of node influence coefficients and applying Fourier transform and inverse transform techniques. …”
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6910
Enhanced multi-component module.
Published 2025“…The aPFGCN module effectively reduces the dimensionality of features and decreases model complexity to obtain the final node feature representation by personalizing the adjustment of node influence coefficients and applying Fourier transform and inverse transform techniques. …”
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6911
The architecture of the TCBiL.
Published 2025“…The aPFGCN module effectively reduces the dimensionality of features and decreases model complexity to obtain the final node feature representation by personalizing the adjustment of node influence coefficients and applying Fourier transform and inverse transform techniques. …”
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6912
Detail of the encoder.
Published 2025“…The aPFGCN module effectively reduces the dimensionality of features and decreases model complexity to obtain the final node feature representation by personalizing the adjustment of node influence coefficients and applying Fourier transform and inverse transform techniques. …”
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6913
Detail of the Fourier transform.
Published 2025“…The aPFGCN module effectively reduces the dimensionality of features and decreases model complexity to obtain the final node feature representation by personalizing the adjustment of node influence coefficients and applying Fourier transform and inverse transform techniques. …”
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6914
Detail of the decoder.
Published 2025“…The aPFGCN module effectively reduces the dimensionality of features and decreases model complexity to obtain the final node feature representation by personalizing the adjustment of node influence coefficients and applying Fourier transform and inverse transform techniques. …”
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6915
Encoder-decoder architecture.
Published 2025“…The aPFGCN module effectively reduces the dimensionality of features and decreases model complexity to obtain the final node feature representation by personalizing the adjustment of node influence coefficients and applying Fourier transform and inverse transform techniques. …”
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6916
Dataset description.
Published 2025“…The aPFGCN module effectively reduces the dimensionality of features and decreases model complexity to obtain the final node feature representation by personalizing the adjustment of node influence coefficients and applying Fourier transform and inverse transform techniques. …”
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6917
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6918
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6919
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6920