Active learning for protein function prediction in protein-protein interaction networks

The high-throughput technologies have led to vast amounts of protein-protein interaction (PPI) data, and a number of approaches based on PPI networks have been proposed for protein function prediction. However, these approaches do not work well if annotated or labeled proteins are scarce in the networks. To address this issue, we propose an active learning based approach that uses graph-based centrality metrics to select proper candidates for labeling. We first cluster a PPI network by using the spectral clustering algorithm and select some informative candidates for labeling within each cluster according to a certain centrality metric, and then apply a collective classification algorithm to predict protein function based on these labeled proteins. Experiments over two real datasets demonstrate that the active learning based approach achieves a better prediction performance by choosing more informative proteins for labeling. Experimental results also validate that betweenness centrality is more effective than degree centrality and closeness centrality in most cases.