Protein-protein interaction prediction via Collective Matrix Factorization

• Published in: 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) pp. 62 - 67
• Main Authors: Qian Xu, Xiang, E W, Qiang Yang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2010
• Subjects: Bioinformatics; Collective Matrix Factorization; Genomics; Humans; Knowledge engineering; protein-protein interactions; Proteins; Sparse matrices; Support vector machines; transfer learning
• ISBN: 1424483069; 9781424483068
• DOI: 10.1109/BIBM.2010.5706537

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 well-known Collective Matrix Factorization (CMF) technique to 'transfer' usable linkage knowledge from relatively dense interaction network to a sparse target network. Our approach is to establish the correspondence between a source and a target network via network similarities. We test this method on two real protein-protein interaction networks, Helicobacter pylori (as a target network) and Human (as a source network). Our experimental results show that our method can achieve higher and more robust performance as compared to some baseline methods.