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Related Concept Videos

Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Protein-protein Interfaces

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Synthetic Biology02:55

Synthetic Biology

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Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

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IP3/DAG Signaling Pathway01:11

IP3/DAG Signaling Pathway

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Related Experiment Video

Updated: Jul 4, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Using directed information to build biologically relevant influence networks.

Arvind Rao1, Alfred O Hero, David J States

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

Journal of Bioinformatics and Computational Biology
|June 25, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a new network inference method using directed information (DTI) to build biologically accurate gene regulatory networks. The approach enhances understanding of gene interactions in processes like T-cell activation and kidney development.

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Area of Science:

  • Computational Biology
  • Systems Biology
  • Genomics

Background:

  • Inferring biologically relevant influence networks from high-throughput data is challenging.
  • Probabilistic models offer plausible network structures but lack biological interpretability.
  • Existing methods struggle to provide experimentally verifiable biological insights.

Purpose of the Study:

  • To develop a novel network inference methodology that integrates biological transcription processes.
  • To enable experimentally verifiable inference of gene regulatory networks.
  • To explore specific gene interactions beyond data-driven discoveries.

Main Methods:

  • Proposed a network inference methodology based on the directed information (DTI) criterion.
  • Developed supervised and unsupervised variants of network inference via DTI.
  • Applied the framework to publicly available embryonic kidney and T-cell microarray datasets.

Main Results:

  • Inferred gene networks relevant to mammalian nephrogenesis and T-cell activation.
  • Demonstrated conformity of inferred interactions with existing literature.
  • Compared the DTI method with the coefficient of determination (CoD) method, showing favorable results.
  • Proposed a DTI-based framework for resolving transcription factor module and target gene interactions.
  • Showcased DTI's utility with mutual information for inferring higher-order gene interaction networks.

Conclusions:

  • The proposed DTI-based methodology enhances the biological interpretability of inferred gene networks.
  • The framework facilitates experimentally verifiable network inference and exploration of specific gene interactions.
  • Directed information offers a robust criterion for understanding complex gene regulatory mechanisms and cooperative interactions.