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Bayesian module identification from multiple noisy networks

Overview of attention for article published in EURASIP Journal on Bioinformatics & Systems Biology, February 2016
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Bayesian module identification from multiple noisy networks
Published in
EURASIP Journal on Bioinformatics & Systems Biology, February 2016
DOI 10.1186/s13637-016-0038-9
Pubmed ID

Siamak Zamani Dadaneh, Xiaoning Qian


Module identification has been studied extensively in order to gain deeper understanding of complex systems, such as social networks as well as biological networks. Modules are often defined as groups of vertices in these networks that are topologically cohesive with similar interaction patterns with the rest of the vertices. Most of the existing module identification algorithms assume that the given networks are faithfully measured without errors. However, in many real-world applications, for example, when analyzing protein-protein interaction networks from high-throughput profiling techniques, there is significant noise with both false positive and missing links between vertices. In this paper, we propose a new model for more robust module identification by taking advantage of multiple observed networks with significant noise so that signals in multiple networks can be strengthened and help improve the solution quality by combining information from various sources. We adopt a hierarchical Bayesian model to integrate multiple noisy snapshots that capture the underlying modular structure of the networks under study. By introducing a latent root assignment matrix and its relations to instantaneous module assignments in all the observed networks to capture the underlying modular structure and combine information across multiple networks, an efficient variational Bayes algorithm can be derived to accurately and robustly identify the underlying modules from multiple noisy networks. Experiments on synthetic and protein-protein interaction data sets show that our proposed model enhances both the accuracy and resolution in detecting cohesive modules, and it is less vulnerable to noise in the observed data. In addition, it shows higher power in predicting missing edges compared to individual-network methods.

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 18%
Unknown 9 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 36%
Professor > Associate Professor 3 27%
Student > Ph. D. Student 2 18%
Student > Bachelor 1 9%
Unknown 1 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 3 27%
Computer Science 2 18%
Mathematics 1 9%
Economics, Econometrics and Finance 1 9%
Social Sciences 1 9%
Other 1 9%
Unknown 2 18%