Sub-modular resolution analysis by network mixture models

Elisabetta Marras, Antonella Travaglione, Enrico Capobianco

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Inferring the structure of networks usually involves the attempt of retrieving their modular organization and knowing its possible interpretation, while quantifying the involved computational complexity through the methods and algorithms to be used. In protein interactomics, it is assumed that even the most recently created interactomes are known only up to a certain degree of coverage and accuracy, due to both experimental and computational limitations. Therefore, we need to infer from the measured interactomes about real interactomes as much as we infer from samples relative to a reference population. In order to exploit additional information sources, it is common to integrate multiple omic data and pursue method fusion. Particularly after the advent of high-throughput technologies, the increased complexity of data-intensive applications has determined an important role for network inference. Consequently, advances in spectral clustering, community detection algorithms and modularity optimization methods have been proposed, according to both deterministic and probabilistic solutions. We have considered the two kinds of approaches, and applied some of the available methods to two human interactomes obtained from high-throughput small-scale experiments and mass spectrometry measurements. The main motivation of this study is refining the resolution spectrum at which protein modularity maps can be studied. First, we started by a coarse-grained interactome decomposition through core and community structures, and by applying sub-sampling to the interactome adjacency matrix. Then, we switched to stochastic methods to uncover fine-grained interactome components, and applied both variational and mixture statistical models. Lastly, we integrated our analysis with the biological validation of the retrieved modules. Overall, the proposed approach shows potential for calibrating modularity detection in protein interactomes at different resolutions.

Original languageEnglish (US)
Article number19
JournalStatistical Applications in Genetics and Molecular Biology
Volume9
Issue number1
DOIs
StatePublished - 2010
Externally publishedYes

Keywords

  • Biological networks
  • Biological validation
  • Interactome modularity
  • Mixture models
  • Variational learning

ASJC Scopus subject areas

  • Statistics and Probability
  • Molecular Biology
  • Genetics
  • Computational Mathematics

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