Exact(7)
Whereas these results already show that a naive Bayes classifier can yield interesting insights, in the following we will present a more sophisticated Machine Learning approach, taking multiple network centrality features into account and improving classification errors.
Centrality features were calculated to additionally estimate the load of the reactions.
For calculating the centrality features and the clustering coefficients, we represented the metabolic network as an undirected graph, also known as a reaction-pair network.
We computed the centrality features by using the R package 'igraph' [ 59] consisting of betweenness centrality (BC), closeness centrality (CN), eccentricity (EC) [ 10], and eigenvector centrality (EV) [ 60].
The random forest model with node and edge centrality features consistently outperforms random assignment at all threshold levels with a significance of P < 0.01 by the Wilcoxon signed-rank test.
Except for the centrality features and clustering coefficients, the topology features were calculated by a representation of the network as a bipartite graph consisting of metabolites and reactions as alternating nodes.
Similar(53)
To facilitate the identification of nodes with the highest scores we applied the 'plot by centrality' feature of CentiScaPe.
Centroid is a complex centrality feature based on the ranking of how many nodes are closer to the gene than other genes in the network.
Thus, the main use of the 'plot by centrality' feature is to identify group of nodes clustered according to combination of specific topological and/or experimental properties, in order to extract sub-networks to be further analyzed.
Thus, as an alternative we test network construction and centrality feature selection algorithms for simulated ER networks, which use a uniform probability to determine whether or not nodes are connected.
Using degree and centrality as features, Zhu et al. [ 31] trained a SVM classifier to rank potential drug targets and achieved promising results.
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