Our results show that the precision of the networks obtained with PriorPC is greatly improved over that of the networks obtained with PC, for every dataset.
In addition, we did not subdivide the tissue to certain cell type when we constructed tissue TRN, which may limit the precision of the networks.
However, when the network size becomes larger, the precision of the preprocessed network (4.0% in the 50-node case and 2.4% in the 100-node case) gradually drops to the random guess precision (4.4% and 1.8%, respectively), as shown in Figure 5.
Higher feature dimension will influence the design of neural network, and fewer numbers of samples will cause network training incomplete or over-fitted, both of which restrict the recognition precision of the neural network.
One measure that we use is the precision of the inferred network, equal to the number of true positives divided by the total number of edges in the inferred network.
In order to examine the measurement accuracy and precision of the neural network method, computer simulation was extensively performed for various combinations of crack opening levels and signal-to-noise (S/N) ratios.
Precision of the new network is minimally influenced by masking of this single term.
In other words, AUPR represents the average precision of the given network distance measure.
In tests of the MIPS functional linkage reference set that included linkages derived using the functional category "protein synthesis", the precision of the two networks is comparable, while coverage for both genes and reference linkages is significantly improved for the new network model (Figure 5A).
The dot-connected lines are averaged precision or recall of the network learned with 1,000 sets of prior knowledge.
Therefore, the convergence rate and approximation precision of the RBF neural network are improved significantly.
