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Section 3 reports results and discussions using different datasets, network inference approaches and classification models.
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The larger dataset network tends to have lower coverage of disease classes.
The comparison of network topologies with RD and SAD helped to test for the hybridization scenario best fitting the natural dataset network topology.
For each time course dataset, Network-Identifier enumerates all possible regulatory forms on each target gene.
For the larger dataset networks, success rate and the coverage of disease classes become lower but prediction of more plant-disease relations can be achieved.
As expected, we found there were generally higher network relatedness scores (R, definition are provided in the Materials and Methods section) between the dataset networks from the same cancer type compared to different cancer types.
As a consequence, for the larger dataset networks, the success rate becomes lower and the coverage of disease classes is lower but prediction of more plant-disease relations can be achieved.
The value R ij in the network relatedness matrix is calculated as follows: (8) R ij = N 0 i j N 1 i j Where N0 ij is the count of the shortest path whose length was not beyond a cutoff (0, 1, or 2) between nodes from ith and jth dataset networks, and N 1ij is this count in their combined network.
Vanted offers the integration of various datasets into network nodes and edges (data mapping) thereby enabling a network-based view on large-scale datasets.
The synthetic datasets and network were generated using GeneNetWeaver from DREAM (Dialogue for Reverse Engineering Assessments and Methods) projects [ 18].
More stringent selection criteria were not used in this study to avoid the risk of Type 2 error and unnecessarily limit the datasets for network analysis [ 90, 91].
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