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Evaluations show that, compared to networks built with ad-hoc design method, networks constructed by our design approach can efficiently use network equipment in various environments.
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Furthermore, the sorting achieved twofold or greater improvements over the typical parallel sort method, network of workstations (NOW -sort.
We named this method " Network-Inference with Multi Objective Optimization" (NIMOO).
In this paper we presented the gene regulatory network inference method "Network Inference with Multi Objective Optimization" (NIMOO).
The concept was implemented by Dutkowski and Ideker in the method Network-guided forest [ 28].
We call this method Network-based Hidden Key Molecule Miner (NetHiKe), and a detailed description is provided in the "Methods" section.
Therefore, a non-random sampling method, network sampling, was applied, which was based on referrals from early members (key persons) already in the sample.
These centrality measures were compared with other typical centrality measures: Local Average Connectivity- (LAC-) based method, Network Centrality (NC), Subgraph Centrality (SC), and Information Centrality (IC).
For all the remaining methods, networks were inferred using the complete dataset (all perturbations simultaneously).
The advantages and disadvantages of the previous methods offer some insight into the design of an effective network sparsification method for networks.
Research interests: QSAR, applicability domain, data mining methods, network protocols.
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