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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,...
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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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Related Experiment Video

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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Published on: December 7, 2021

Predicting essential genes based on network and sequence analysis.

Yih-Chii Hwang1, Chen-Ching Lin, Jen-Yun Chang

  • 1Institute of Biomedical Informatics, Center for Systems and Synthetic Biology, National Yang-Ming University, Taipei, 112, Taiwan.

Molecular Biosystems
|May 20, 2009
PubMed
Summary

Essential genes are crucial for organism survival. This study identifies key topological and sequence properties to predict essential genes, aiding drug development.

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Last Updated: Jun 23, 2026

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Area of Science:

  • Systems Biology
  • Genomics
  • Bioinformatics

Background:

  • Essential genes are critical for cellular viability and understanding them is key to systems biology.
  • Predicting essential genes in pathogens is vital for targeted antimicrobial drug development.
  • Protein-protein interaction (PPI) networks offer insights into gene relationships, with essential genes often showing higher connectivity.

Purpose of the Study:

  • To systematically investigate the topological properties of essential versus nonessential genes in PPI networks.
  • To identify sequence properties associated with gene essentiality.
  • To develop a machine learning model for predicting essential genes using network and sequence data.

Main Methods:

  • Analysis of protein-protein interaction networks in Saccharomyces cerevisiae and Escherichia coli.
  • Examination of topological features (e.g., connectivity, network roles) of essential and nonessential genes.
  • Assessment of sequence properties (e.g., ORF length, phyletic retention) for correlation with gene essentiality.
  • Development of a machine learning classifier integrating topological and sequence features.

Main Results:

  • Essential genes occupy topologically significant roles within PPI networks.
  • Several topological features were found to be statistically significant in distinguishing essential from nonessential genes.
  • Sequence properties like ORF length and phyletic retention showed associations with gene essentiality.
  • A predictive model demonstrated the capability to identify essential genes using combined features.

Conclusions:

  • Topological and sequence properties are valuable indicators of gene essentiality.
  • Computational prediction of essential genes can complement experimental methods.
  • This approach holds promise for accelerating antimicrobial drug discovery by identifying essential pathogen genes.