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Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple

Peizhuo Wang1, Lin Gao2, Yuxuan Hu1

  • 1School of Computer Science and Technology, Xidian University, Xi'an, 710071, China.

BMC Bioinformatics
|October 31, 2018
PubMed
Summary

We developed ConMod, an efficient method to find conserved functional modules across multiple biological networks. This approach improves accuracy and speed for analyzing complex biological systems and identifying shared biological functions.

Keywords:
Conserved modulesFeaturesMatrix factorizationMultiple biological networks

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Analyzing multi-omics data across conditions is crucial for understanding biological mechanisms at a systems level.
  • Multi-layer network models offer insights into simultaneous data analysis, aiding in the identification of conserved functional modules.
  • Detecting conserved functional modules in large, complex multiple biological networks presents significant accuracy and efficiency challenges.

Purpose of the Study:

  • To propose an efficient method, ConMod, for discovering conserved functional modules within multiple biological networks.
  • To address the challenges of accuracy and efficiency in detecting conserved modules in complex, multi-network biological data.

Main Methods:

  • ConMod characterizes multiple networks using two features, compressing them into feature matrices.
  • Module detection is performed on these feature matrices via multi-view non-negative matrix factorization (NMF).
  • The NMF approach is independent of the number of input networks, enhancing scalability.

Main Results:

  • ConMod demonstrated improved accuracy and efficiency in identifying conserved modules compared to state-of-the-art methods on synthetic and real biological networks.
  • Application to cancer co-expression networks revealed shared gene modules with significant functional implications, including ribosome biogenesis and immune response.
  • Analysis of brain tissue-specific protein interaction networks identified conserved modules related to nervous system development and mRNA processing.

Conclusions:

  • ConMod efficiently identifies conserved modules across any number of networks with low time and space complexity.
  • The method serves as a valuable tool for inferring shared traits and biological functions across multiple biological systems.
  • ConMod facilitates a deeper understanding of biological mechanisms by analyzing complex, multi-network data.