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We conclude that our method has computational complexity that scales linearly with the number of interactions.
This method has computational complexity (O(N^{3})), where N is the size of the matrix.
Our method has computational and practical advantages over competing methods.
Similar(9)
When the number of the insertions is zero, the MDS-method has computational difficulty because the network is not well-connected especially after a certain amount of PPIs is deleted, which does not satisfy the conditions of PPI network required by the MDS-method.
Also in this case, the SWEM and the SWPM appear to be the slowest methods, whereas the advanced parametric methods had computational times that were significantly less than those of SWEM and SWPM.
These methods have computational feasibility and give nearly nominal coverage rates.
Even though some of these methods have computational gap filling methods, there is still a need for manual curation to obtain a functional model.
The presented method has high computational efficiency, and the computational accuracy is also verified.
Our hybrid method has reasonable computational complexity both in training and at run time, and yields excellent results in practice.
Numerical experiments have demonstrated that the proposed method has excellent computational efficiency and satisfactory identification performance.
This modeling method has high computational efficiency and clear physical meanings.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com