Predicting protein-protein interactions from multimodal biological data sources via nonnegative matrix tri-factorization

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
Hua WangFeiping Nie


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 Caenorhabd...Continue Reading


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Datasets Mentioned


Methods Mentioned

phylogenetic profile
Rosetta Stone
in silico methods
interaction prediction

Related Concepts

Drosophila melanogaster
Saccharomyces cerevisiae
Caenorhabditis elegans
Protein Interaction Mapping
Protein Interaction Maps
Combined Modality Therapy
Drosophila melanogaster
Extracellular Matrix
Gene Expression
Saccharomyces cerevisiae

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