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Using directed information to build biologically relevant influence networks.

Arvind Rao1, Alfred O Hero, David J States

  • 1Electrical Engineering and Computer Science, Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA. ukarvind@umich.edu

Computational Systems Bioinformatics. Computational Systems Bioinformatics Conference
|October 24, 2007
PubMed
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This study introduces a novel network inference method using directed information (DTI) to improve biological accuracy in computational biology. The approach enhances understanding of gene regulatory networks and specific molecular interactions.

Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Inferring biologically relevant influence networks is a significant challenge in computational biology.
  • Probabilistic models using high-throughput data offer plausible network structures but lack robust biological interpretation.
  • Existing methods struggle to provide experimentally verifiable biological insights into complex processes.

Purpose of the Study:

  • To develop a network inference methodology that integrates transcriptional biology for experimentally verifiable results.
  • To propose a framework based on the directed information (DTI) criterion for more accurate biological network inference.
  • To enable the exploration of specific molecular interactions beyond data-driven discoveries.

Main Methods:

Related Experiment Videos

  • Developed a network inference methodology based on the directed information (DTI) criterion.
  • Incorporated the biology of transcription into the inference framework.
  • Applied supervised and unsupervised variants of DTI network inference to microarray datasets.

    • Main Results:

    • Inferred biologically relevant networks for mammalian nephrogenesis and T-cell activation using DTI.
    • Demonstrated conformity of inferred interactions with existing literature.
    • Compared DTI method performance against the coefficient of determination (CoD) method, showing improved biological relevance.

    Conclusions:

    • The proposed DTI-based network inference methodology enhances the biological interpretability of gene regulatory networks.
    • This framework facilitates experimentally verifiable inference and exploration of specific biological interactions.
    • The approach provides a more robust tool for understanding complex biological systems from high-throughput data.