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Predicting Protein–Protein Interactions from Multimodal Biological Data Sources via Nonnegative Matrix Tri-Factorization
- Source :
- Journal of Computational Biology. 20:344-358
- Publication Year :
- 2013
- Publisher :
- Mary Ann Liebert Inc, 2013.
-
Abstract
- 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.
- Subjects :
- Computer science
Saccharomyces cerevisiae
Computational biology
Interactome
Non-negative matrix factorization
Protein–protein interaction
Protein structure
Protein Interaction Mapping
Genetics
Animals
Humans
Protein Interaction Maps
Nonnegative matrix
Caenorhabditis elegans
Databases, Protein
Molecular Biology
Biological data
Matrix completion
biology
business.industry
Proteins
Reproducibility of Results
Pattern recognition
biology.organism_classification
Computational Mathematics
Drosophila melanogaster
Computational Theory and Mathematics
Modeling and Simulation
Artificial intelligence
business
Algorithms
Subjects
Details
- ISSN :
- 15578666 and 10665277
- Volume :
- 20
- Database :
- OpenAIRE
- Journal :
- Journal of Computational Biology
- Accession number :
- edsair.doi.dedup.....a9c242ba1eb4ece404dcbf5dd3e93f9b