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Shortest path counting in probabilistic biological networks.

Yuanfang Ren1, Ahmet Ay2, Tamer Kahveci3

  • 1Department of Computer and Information Science and Engineering, University of Florida, Gainesville, 32611, FL, USA.

BMC Bioinformatics
|December 6, 2018
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Summary

This study introduces a new method for counting shortest paths in uncertain biological networks. Our approach accurately identifies key network components and functional pathways, outperforming existing methods.

Keywords:
Community detectionEdge betweennessProbabilistic networksShortest path

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Area of Science:

  • Computational Biology
  • Network Science
  • Bioinformatics

Background:

  • Biological regulatory networks govern cellular activities.
  • Understanding network structure via shortest path analysis is crucial.
  • Network uncertainty complicates shortest path calculations.

Purpose of the Study:

  • To develop a novel method for counting shortest paths in probabilistic biological networks.
  • To address limitations of existing methods designed for deterministic networks.

Main Methods:

  • Developed a new mathematical model for expressing and computing shortest paths in probabilistic networks.
  • Proved the correctness of the proposed mathematical model.

Main Results:

  • The novel method accurately counts shortest paths in probabilistic networks.
  • Outperformed existing methods in accuracy on synthetic and real gene regulatory networks.
  • Demonstrated scalability for analyzing large biological networks.

Conclusions:

  • The method successfully detects communities in probabilistic networks.
  • Identified key functional characteristics in cell cycle pathways across cancer types.
  • Provides a robust tool for analyzing uncertain biological network structures.