Protein-protein interaction prediction via Collective Matrix Factorization

Abstract--Protein-protein interactions (PPI) play an important role in cellular processes and metabolic processes within a cell. An important task is to determine the existence of interactions among proteins. Unfortunately, existing biological experimental techniques are expensive, time-consuming and labor-intensive. The network structures of many such networks are sparse, incomplete and noisy, containing many false positive and false negatives. Thus, state-of-the-art methods for link prediction in these networks often cannot give satisfactory prediction results, especially when some networks are extremely sparse. Noticing that we typically have more than one PPI network available, we naturally wonder whether it is possible to 'transfer' the linkage knowledge from some existing, relatively dense networks to a sparse network, to improve the prediction performance. Noticing that a network structure can be modeled using a matrix model, in this paper, we introduce the wellknown C...

• Type: Journal
• Year: 2010
• Where: BIBM
• Authors: Qian Xu, Evan Wei Xiang, Qiang Yang
• Keywords: BIBM 2010, Bioinformatics, Collective Matrix Factorization, Network Structure, Target Network