José Luis Oliveira
Universidade de Aveiro, DETI / IEETA
3810-193 Aveiro, Portugal
(+351) 234 370 500
Organization Measurement (OM) method
The complete protein-protein interaction (PPI) network of even the most studied organisms is yet to be fully established. This is mostly due to lack of reliability and accuracy of the high-throughput experimental methods used for PPI identification. PPIs can be conveniently represented as networks, allowing the use of graph theory in their study. Different network-based methods have been used to identify false-positive interactions and missing links in biological networks. Network topology studies may reveal patterns associated with specific organisms or the type of PPIs. Thus, in this paper, we propose a new methodology to denoise PPI networks and predict missing links solely based on the network topology, the Organization Measurement (OM) method.
The OM methodology was applied in the denoising of Saccharomyces cerevisiae (Yeast and CS2007) and Homo sapiens (Human). To evaluate our methodology, two strategies were used. The first compared its application in random networks and in the gold standard networks, while the second perturbed the networks with the gradual random addition and removal of edges. The applied validation strategy showed that the proposed methodology achieves an AUC of 0.95 and 0.87, in Yeast and Human networks, respectively. The random removal of 80% of the Yeast gold standard interactions resulted in an AUC of 0.71, whereas the random addition of 80% interactions resulted in an AUC of 0.75. In Human, the random removal of 80% interactions resulted in an AUC of 0.62, while the random addition of 80% interactions resulted in an AUC of 0.72. Applying the OM methodology to the CS2007 dataset yields an AUC of 0.99. We also perturbed the network of the CS2007 dataset by randomly inserting and removing edges in the same proportions previously described. The percentage of false positives identified and removed from the network varied from 97%, when inserting 20% more edges, to 89% when 80% more edges were inserted. The percentage of true positives identified and inserted in the network varied from 95% when removing 20% of the edges, to 40% after the random deletion 80% edges.
The implemented tests show that the OM methodology is sensitive to the topological structure of the biological networks and can be used for network denoising. The obtained results suggest that the present approach can efficiently denoise PPI networks and that it can be applied to different organisms, as long as they have inherent patterns in the structures of their network models. In addition, although the performance of the method correlates with the initial quality of the network, improvements were consistently obtained.