Interspecies Translation of Disease Networks Increases Robustness and Predictive Accuracy
Figure 3
Performance of the naïve Dandelion algorithm on constructing disease networks that are learnt on human and evaluated on human, mouse and Drosophila datasets.
A) The average Sum of Squared Error (SSE) for prediction of the disease phenotype (OPMD vs. control) given the gene expression profiles within the disease networks learnt on human. The cross-validation set which is used during the training phase is depicted by C.V. and the independent test sets are grouped as IND. Test Sets. B) ROC space demonstrates the relative sensitivity and specificity of the generated networks in predicting the disease phenotype. The results from random expectations are illustrated by the red dash-line. C) Number of relationships between genes and the class node, after applying confidence thresholds, are depicted in line per species. D) The number of links found after interspecies translation and optimization of the disease networks within each species. The orange section, separated by red dash-line, represents the number of links that can be found in all species with the confidence threshold of 0.1. E) The interspecies disease domain is generated according to the Markov blanket criteria, after applying the confidence threshold of 0.1.