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The change in the full set normalized coloring was caused by a positive weighting of a large substructure of ligand L C. Re-weighting large substructures does not substantially influence weight differences within the molecule.
The expression levels of these markers from RPPA were weighted equally but in opposing directions for their association with either the luminal A (positive weighting) or luminal B (negative weighting) subtype and summed to create a classifier, by using the predefined log mean centered "luminalness" score cutoff of -0.907.
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The general idea is to form a scalar optimization problem by multiplying each objective function with a positive weight and summing up the weighted objectives.
A digraph G is weighted if each arc ((j, i)) is assigned a positive weight.
A directed digraph (mathcal{G}) is weighted if each arc ((j,i)) is assigned a positive weight (a_{ij}).
A graph (mathcal{G}) is weighted if each arc ((j,i)) is assigned a positive weight (a_{ij}).
If we assign a positive weight to each arc, then the digraph G is said to be weighted.
We first give some perturbational results on the (signless) Laplacian spectral radius of weighted graphs when some weights of edges are modified; we then determine the weighted tree with the largest Laplacian spectral radius in the set of all weighted trees with a fixed number of pendant vertices and a positive weight set.
To have orthogonality with respect to a positive weight function, we need to impose (alpha>-1).
Let ω be a positive weight on Ω with (int_{Omega}omega(t),dmu(t)=1).
To have the orthogonality with respect to a positive weight function, we need to impose (d<0).
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