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MATria: a unified centrality algorithm.

Trevor Cickovski1, Vanessa Aguiar-Pulido2, Giri Narasimhan3

  • 1Bioinformatics Research Group (BioRG) & Biomolecular Sciences Institute, School of Computing & Information Sciences, Florida International University, 11200 SW 8th St, Miami, 33199, FL, USA. tcickovs@fiu.edu.

BMC Bioinformatics
|June 7, 2019
PubMed
Summary
This summary is machine-generated.

We developed MATRIA, an iterative algorithm that unifies centrality measures in biological networks. This approach improves node ranking and agreement between different centrality algorithms, with enhanced speed via GPU parallelism.

Keywords:
CentralityGraphics Processing Unit (GPU)IterationNetworks

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Area of Science:

  • Network Science
  • Computational Biology
  • Graph Theory

Background:

  • Centrality algorithms are key in social network analysis for identifying important nodes.
  • Defining node importance in biological networks is challenging, complicating the selection of appropriate centrality algorithms.
  • Existing centrality methods may yield disparate results due to varied importance definitions.

Purpose of the Study:

  • To generalize and unify the results from multiple k-centrality algorithms.
  • To introduce a novel iterative algorithm, MATRIA, for ranking central nodes in biological networks.
  • To enhance the computational efficiency of centrality analysis through parallel processing.

Main Methods:

  • Developed an iterative algorithm named MATRIA.
  • Generalized results from various k-centrality algorithms into a single ranked list.
  • Implemented GPU parallelism to accelerate the computation.

Main Results:

  • MATRIA produced a unified and ranked set of central nodes across tested biological networks.
  • Demonstrated strong and balanced correlations between MATRIA's output and results from multiple k-centrality algorithms.
  • Achieved significant speed improvements using GPU parallelism.

Conclusions:

  • Iterative processing is effective in mitigating spatial bias and increasing algorithm agreement.
  • MATRIA offers a robust method for unifying centrality measures in complex biological networks.
  • GPU-accelerated MATRIA is scalable for analyzing large biological networks.