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Network enhancement as a general method to denoise weighted biological networks

Author

Listed:
  • Bo Wang

    (Stanford University)

  • Armin Pourshafeie

    (Stanford University)

  • Marinka Zitnik

    (Stanford University)

  • Junjie Zhu

    (Stanford University)

  • Carlos D. Bustamante

    (Stanford University
    Chan Zuckerberg Biohub)

  • Serafim Batzoglou

    (Stanford University
    Illumina Inc)

  • Jure Leskovec

    (Stanford University
    Chan Zuckerberg Biohub)

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.

Suggested Citation

  • Bo Wang & Armin Pourshafeie & Marinka Zitnik & Junjie Zhu & Carlos D. Bustamante & Serafim Batzoglou & Jure Leskovec, 2018. "Network enhancement as a general method to denoise weighted biological networks," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
  • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-05469-x
    DOI: 10.1038/s41467-018-05469-x
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    Cited by:

    1. Majid Noroozi & Marianna Pensky, 2022. "The Hierarchy of Block Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 64-107, June.
    2. Xinjun Li & Fan Feng & Hongxi Pu & Wai Yan Leung & Jie Liu, 2021. "scHiCTools: A computational toolbox for analyzing single-cell Hi-C data," PLOS Computational Biology, Public Library of Science, vol. 17(5), pages 1-14, May.
    3. Yu, Jiating & Leng, Jiacheng & Sun, Duanchen & Wu, Ling-Yun, 2023. "Network Refinement: Denoising complex networks for better community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 617(C).
    4. Vincent Miele & Catherine Matias & Stéphane Robin & Stéphane Dray, 2019. "Nine quick tips for analyzing network data," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-10, December.
    5. Wang, Zhixiao & Rui, Xiaobin & Yuan, Guan & Cui, Jingjing & Hadzibeganovic, Tarik, 2021. "Endemic information-contagion outbreaks in complex networks with potential spreaders based recurrent-state transmission dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    6. Guo, Xiao & Zhang, Hai & Chang, Xiangyu, 2024. "On the efficacy of higher-order spectral clustering under weighted stochastic block models," Computational Statistics & Data Analysis, Elsevier, vol. 190(C).
    7. Majid Noroozi & Ramchandra Rimal & Marianna Pensky, 2021. "Estimation and clustering in popularity adjusted block model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(2), pages 293-317, April.
    8. Markovič, Rene & Gosak, Marko & Grubelnik, Vladimir & Marhl, Marko & Virtič, Peter, 2019. "Data-driven classification of residential energy consumption patterns by means of functional connectivity networks," Applied Energy, Elsevier, vol. 242(C), pages 506-515.

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