Predicting Protein-Protein Interactions from Multimodal Biological Data Sources via Nonnegative Matrix Factorization

Hua Wang, Feiping Nie, Heng Huang, Chris Ding

JCB - 2013

Protein interactions are central to all the biological processes and structural scaffolds in living organisms, because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Several high-throughput methods, for example, yeast two-hybrid system and mass spectrometry method, can help determine protein interactions, which, however, suffer from high false-positive rates. Moreover, many protein interactions predicted by one method are not supported by another. Therefore, computational methods are necessary and crucial to complete the interactome expeditiously. In this work, we formulate the problem of predicting protein interactions from a new mathematical perspective—sparse matrix completion, and propose a novel nonnegative matrix factorization (NMF)-based matrix completion approach to predict new protein interactions from existing protein interaction networks. Through using manifold regularization, we further develop our method to integrate different biological data sources, such as protein sequences, gene expressions, protein structure information, etc. Extensive experimental results on four species, Saccharomyces cerevisiae, Drosophila melanogaster, Homo sapiens, and Caenorhabditis elegans, have shown that our new methods outperform related state-of-the-art protein interaction prediction methods.


Cite this paper

MLA Copied to clipboard!
Wang, Hua, et al. "Predicting protein–protein interactions from multimodal biological data sources via nonnegative matrix tri-factorization." Journal of Computational Biology 20.4 (2013): 344-358.
BibTeX Copied to clipboard!
  title={Predicting protein--protein interactions from multimodal biological data sources via nonnegative matrix tri-factorization},
  author={Wang, Hua and Huang, Heng and Ding, Chris and Nie, Feiping},
  journal={Journal of Computational Biology},
  publisher={Mary Ann Liebert, Inc. 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA}