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Note that in an edge independent path decomposition, the estimate based on the subnet may be different from the estimate based on the independent paths if some paths share a common intermediate vertex.
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Predicting PPIs based on the subnets decomposed from the complex is also O(n).
According to the different structures of protein complexes, it is more feasible to predict the PPIs based on the densely partial subnets in the complexes, which always include areas of tight connection regardless of the differences in the size, number, and distribution of the topological structure.
Step 3. Determine weighted factor ω based on the one-step prediction result of each subnet.
The analysis of the hierarchical net is based on the analysis of its subnets.
In order to guarantee a higher recognition and reliability rates for the cascaded method, the SMNN is combined with a second subnet based on the WTA for classification using a second specific rejection threshold.
Typically, this will be decided based on the destination IP address, as one or more subnets can be allocated to the onboard network of a train (see Section 6.1).
We here show the other algorithm based on the outputs (i.e., temporal subnets) obtained in Algorithm 1.
The network is sparsely connected, and broken into two subnets, with the first subnet directly stimulated by the environment, and using compensatory learning based on the strength leaving the neuron.
The second subnet is a competitive neural network (CNN) based on the winner takes all algorithm (WTA) that is used for the classification.
The second subnet initially fires only spontaneously, and uses compensatory learning based on the weight entering the neuron.
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