Identifying Spurious Interactions in the Protein-Protein Interaction Networks Using Local Similarity Preserving Embedding

In recent years, a remarkable amount of protein-protein interaction (PPI) data are being available owing to the advance made in experimental high-throughput technologies. However, the experimentally detected PPI data usually contain a large amount of spurious links, which could contaminate the analysis of the biological significance of protein links and lead to incorrect biological discoveries, thereby posing new challenges to both computational and biological scientists. In this paper, we develop a new embedding algorithm called local similarity preserving embedding (LSPE) to rank the interaction possibility of protein links. By going beyond limitations of current geometric embedding methods for network denoising and emphasizing the local information of PPI networks, LSPE can avoid the unstableness of previous methods. We demonstrate experimental results on benchmark PPI networks and show that LSPE was the overall leader, outperforming the state-of-the-art methods in topological false links elimination problems.