Abstract
Interactomes such as Protein interaction networks have many undiscovered links between entities. Experimental verification of every link in these networks is prohibitively expensive, and therefore computational methods to direct the search for possible links are of great value. The problem of finding undiscovered links in a network is also referred to as the link prediction problem. A popular approach for link prediction has been to formulate it as a binary classification problem in which class labels indicate the existence or absence of a link (we refer to these as positive links or negative links respectively) between a pair of nodes in the network. Researchers have successfully applied such supervised classification techniques to determine the presence of links in protein interaction networks. However, it is quite common for protein-protein interaction (PPI) networks to have a large proportion of undiscovered links. Thus, a link prediction approach could incorrectly treat undiscovered positive links as negative links, thereby introducing a bias in the learning. In this paper, we propose to denoise the class of negative links in the training data via a Gaussian process anomaly detector. We show that this significantly reduces the noise due to mislabelled negative links and improves the resulting link prediction accuracy. We evaluate the approach by introducing synthetic noise into the PPI networks and measuring how accurately we can reconstruct the original PPI networks using classifiers trained on both noisy and denoised data. Experiments were performed with five different PPI network datasets and the results indicate a significant reduction in bias due to label noise, and more importantly, a significant improvement in the accuracy of detecting missing links via classification.
Highlights
Graphical networks can depict many complex systems involving biological, social and informational connections between entities
Performance evaluation of different anomaly detector We trained different anomaly detection methods on the training set of each protein-protein interaction (PPI) network and measured the performance on corresponding test set, where the training and test sets are constructed as described in Phase-I of the previous subsection
We notice that One Class Gaussian Process anomaly detection technique has a better score than the other anomaly detectors
Summary
Graphical networks can depict many complex systems involving biological, social and informational connections between entities. At the most abstract level, these networks are modelled by graphs in which nodes represent individuals or agents and links denote the interactions or relationships between nodes. Structural properties of biological networks are of great interest as they directly correlate with biological function (Qi and Ge 2006; Wuchty et al 2003). Various attempts have been made to understand the topological evolution of networks (Albert and Barabási 2002; Dorogovtsev and Mendes 2002). The evolution of networks involves two processes: i) the addition or deletion of nodes and ii) The addition or deletion of edges (links) between nodes. The second process of topological evolution when new connections are added to the existing network
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