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Biological network clustering by robust NMF

Published: 20 September 2014 Publication History

Abstract

We propose a Robust Non-negative Matrix Factorization (RNMF) formulation by introducing L1-norm regularization terms for decomposed factors to cluster noisy biological networks for identification of functional modules. To solve robust NMF, we develop an accelerated alternative proximal method, which takes advantages of a fast iterative shrinkage-thresholding strategy to update each factorized component at each step. We compare the performance of this accelerated proximal method with a multiplicative algorithm and a general proximal method for the same RNMF formulation. Experiments on synthetic networks and Protein-Protein Interaction (PPI) networks demonstrate that the accelerated proximal method is superior to the other algorithms in terms of efficiency and effectiveness for functional module identification.

References

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A. Beck and M. Teboulle. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sciences, 2(1):183--202, 2009.
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J. Chan, W. Liu, A. Kan, and et al. Discovering latent blockmodels in sparse and noisy graphs using non-negative matrix factorisation. In ACM International Conference on Information and Knowledge Management, 2013.
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C. Ding, X. He, and H. D. Simon. On the equivalence of nonnegative matrix factorization and spectral clustering. In SIAM International Conference on Data Mining, 2005.
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D. Kuang, C. Ding, and H. Park. Symmetric nonnegative matrix factorization for graph clustering. In Proceedings of the SIAM International Conference on Data Mining, 2012.
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F. Wang, T. Li, X. Wang, and et al. Community discovery using nonnegative matrix factorization. Data Min Knowl Disc, 22(3):493--521, 2011.
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Y. Wang and X. Qian. Clustering of noisy graphs via non-negative matrix factorization with sparsity regularization. under review, 2014.
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Y. Wang and X. Qian. Functional module identification in protein interaction networks by interaction patterns. Bioinformatics, 30(1):81--93, 2014.

Cited By

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  • (2022)Community Detection in Fully-Connected Multi-layer Networks Through Joint Nonnegative Matrix FactorizationIEEE Access10.1109/ACCESS.2022.316865910(43022-43043)Online publication date: 2022
  • (2016)Bayesian module identification from multiple noisy networksEURASIP Journal on Bioinformatics and Systems Biology10.1186/s13637-016-0038-92016:1Online publication date: 5-Feb-2016

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Published In

cover image ACM Conferences
BCB '14: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
September 2014
851 pages
ISBN:9781450328944
DOI:10.1145/2649387
  • General Chairs:
  • Pierre Baldi,
  • Wei Wang
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 September 2014

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Author Tags

  1. accelerated proximal method
  2. functional module identification
  3. multiplicative algorithm
  4. proximal method
  5. robust non-negative matrix factorization

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  • Research-article

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BCB '14
Sponsor:
BCB '14: ACM-BCB '14
September 20 - 23, 2014
California, Newport Beach

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Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

View all
  • (2022)Community Detection in Fully-Connected Multi-layer Networks Through Joint Nonnegative Matrix FactorizationIEEE Access10.1109/ACCESS.2022.316865910(43022-43043)Online publication date: 2022
  • (2016)Bayesian module identification from multiple noisy networksEURASIP Journal on Bioinformatics and Systems Biology10.1186/s13637-016-0038-92016:1Online publication date: 5-Feb-2016

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