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The empirical study shows that the fast approximation algorithm is much faster than the greedy multiple integration algorithm.
Following the above analysis, we conclude that the time complexity of the greedy multiple integration algorithm is the same as the fast approximation algorithm.
We name it fast approximation algorithm because it not only has a lower bound on the results, but also runs faster than the greedy multiple integration algorithm proposed above.
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The production run was performed with the impulse Verlet-I multiple timestep integration algorithm as implemented by NAMD.
In this study, we propose a fast and high performance multiple data integration algorithm for identifying human disease genes.
In this paper, we propose a fast and high performance multiple data integration algorithm to generalize the logistic regression based algorithm in [ 21].
When three biological networks are employed, as illustrated in Figure 5(b), the proposed logistic regression based algorithm using F3 achieves the highest AUC score among all these multiple data integration algorithms.
Instead, it must be numerically integrated using any standard numerical integration algorithm.
The chromatograms were integrated automatically with the Signal Finder integration algorithm of MultiQuant V2.0 software.
The constitutive integration algorithm was discussed.
This is an implicit and unconditionally stable integration algorithm method.
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