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Nat Commun. 2018 Aug 6;9(1):3108. doi: 10.1038/s41467-018-05469-x.

Network enhancement as a general method to denoise weighted biological networks.

Author information

1
Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, 94305, CA, USA.
2
Department of Physics, Stanford University, 382 Via Pueblo Mall, Stanford, 94305, CA, USA.
3
Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, 94305, CA, USA.
4
Department of Biomedical Data Science, Stanford University, 1265 Welch Road, Stanford, 94305, CA, USA.
5
Chan Zuckerberg Biohub, 499 Illinois St, San Francisco, 94158, CA, USA.
6
Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, 94305, CA, USA. serafim@cs.stanford.edu.
7
Illumina Inc, 499 Illinois Street, San Francisco, 94158, CA, USA. serafim@cs.stanford.edu.
8
Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, 94305, CA, USA. jure@cs.stanford.edu.
9
Chan Zuckerberg Biohub, 499 Illinois St, San Francisco, 94158, CA, USA. jure@cs.stanford.edu.

Abstract

Networks are ubiquitous in biology where they encode connectivity patterns at all scales of organization, from molecular to the biome. However, biological networks are noisy due to the limitations of measurement technology and inherent natural variation, which can hamper discovery of network patterns and dynamics. We propose Network Enhancement (NE), a method for improving the signal-to-noise ratio of undirected, weighted networks. NE uses a doubly stochastic matrix operator that induces sparsity and provides a closed-form solution that increases spectral eigengap of the input network. As a result, NE removes weak edges, enhances real connections, and leads to better downstream performance. Experiments show that NE improves gene-function prediction by denoising tissue-specific interaction networks, alleviates interpretation of noisy Hi-C contact maps from the human genome, and boosts fine-grained identification accuracy of species. Our results indicate that NE is widely applicable for denoising biological networks.

PMID:
30082777
PMCID:
PMC6078978
DOI:
10.1038/s41467-018-05469-x
[Indexed for MEDLINE]
Free PMC Article

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