Increasing the reliability of proteinprotein interaction networks via non-convex semantic embedding
Over the last decade, the development of high-throughput techniques has resulted in a rapid accumulation of proteinprotein interaction (PPI) data. However, the high-throughput experimental interaction data is prone to exhibit high level of noise. Despite the promising performance of current geometric approaches for increasing the reliability of PPI networks, it is still of major concern to find a better method that requires less structural assumptions and is more robust against the large fraction of noisy PPIs. In this paper, we propose a new approach called non-convex semantic embedding (NCSE) for assessing the reliability of interactions. Unlike previous approaches which seek to preserve a predefined distance matrix in the embedding space, NCSE tries to adaptively learn a Euclidean embedding under the simple geometric assumption of PPI networks. We also propose using non-convex cost function in order to improve the robustness of NCSE. The experimental results show that our approach substantially outperforms previous methods on PPI assessment problems. NCSE could thus facilitate further graph-based studies of PPIs and may help infer their hidden underlying biological knowledge. The Matlab source code of NCSE is freely available upon request.
proteinprotein interaction (ppi) non-convex semantic embedding (ncse) bioinformatics denoising