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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.
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,...
Synthetic Biology02:55

Synthetic Biology

Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Updated: Jun 23, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Biological Network Inference and analysis using SEBINI and CABIN.

Ronald Taylor1, Mudita Singhal

  • 1Computational Biology and Bioinformatics Group, Computational and Informational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA. ronald.taylor@pnl.gov

Methods in Molecular Biology (Clifton, N.J.)
|April 22, 2009
PubMed
Summary

The SEBINI and CABIN toolkit enables accurate reconstruction of biological networks by evaluating and integrating gene expression and protein interaction data. This integrated approach aids systems biology research and development of novel biological insights.

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Area of Science:

  • Systems biology
  • Bioinformatics
  • Computational biology

Background:

  • Understanding biological networks is crucial for systems biology.
  • In silico inference algorithms and experimental techniques are vital for network reconstruction.
  • Public databases store valuable biological network information.

Purpose of the Study:

  • To introduce the Software Environment for BIological Network Inference (SEBINI) for evaluating and improving network inference algorithms.
  • To present the Collective Analysis of Biological Interaction Networks (CABIN) tool for integrating and analyzing biological network data.
  • To provide a combined SEBINI-CABIN toolkit for more accurate and efficient biological network reconstruction.

Main Methods:

  • SEBINI analyzes high-throughput gene expression, protein abundance, or activation data using various inference algorithms.
  • Algorithm developers can use SEBINI to train and compare methods on simulated data.
  • CABIN integrates and analyzes protein-protein interaction and gene-regulatory evidence from multiple sources.

Main Results:

  • SEBINI provides an interactive environment for deploying and evaluating network inference algorithms.
  • CABIN enhances confidence in inferred network edges and allows integration with public databases.
  • The combined SEBINI-CABIN toolkit facilitates more accurate biological network reconstruction.

Conclusions:

  • The SEBINI-CABIN toolkit offers a comprehensive solution for biological network inference and analysis.
  • This toolkit accelerates the process of reconstructing complex biological networks.
  • It supports both software developers and bioinformaticians in advancing systems biology research.