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MAXCOM is an effective method for the discovery of disease-related protein complexes based on network optimization.
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Automatic adjustment (or self-tuning) of network parameters is often implemented as an iterative process, where parameters are gradually changed with a certain period (referred to as optimization loop) based on network performance statistics.
A modified ant colony optimization algorithm is proposed based on network topology decomposition.
In contrast to FBA-related techniques, EFM and ExPa analyses are only based on network stoichiometry and therefore allow an unbiased analysis without imposing an optimization principle.
This algorithm is based on the network optimization theory discussed above and has been successfully applied to the network optimization for the tracking stations of LEO satellites.
We have recently introduced a systematic two-step approach based on process network optimization techniques [1].
The assessment is based on a network investment optimization model considering multi-period optimal power flow and regulatory constraints.
This crowd management mechanism is based on the optimization of network cost as the goal, in order to solve the network overhead request of network overhead crowd optimization space, improve the speed of the virtual Internet restructuring, and reduce the complexity of network overhead management.
The network is partitioned into sub-networks based on global optimization of clustering when a gene under consideration is shifted between the sub-networks as a test.
The proposed methodologies are inspired by the ground logistics analysis techniques based on linear programming network optimization.
The main intention of this paper is to detect and classify the intrusions in a SCADA network based on the optimization.
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