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Related Concept Videos

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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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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Mapping Dysfunctional Protein-Protein Interactions in Disease
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Graph-based information diffusion method for prioritizing functionally related genes in protein-protein interaction

Minh Pham1, Olivier Lichtarge

  • 1Integrative Molecular and Biomedical Sciences Graduate Program, and Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA, minh.pham@bcm.edu.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 5, 2019
PubMed
Summary
This summary is machine-generated.

Graph-based information diffusion efficiently prioritizes functionally related genes, outperforming shortest path methods in biological network analysis. This approach enhances gene prioritization for pathway and clinical symptom studies.

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Network Biology

Background:

  • Shortest path length methods are common for validating gene functional relationships using biological networks.
  • These methods are computationally intensive and often ignore confidence levels of gene associations.
  • Graph-based information diffusion offers an efficient alternative, utilizing edge confidence weights.

Purpose of the Study:

  • To evaluate if graph-based information diffusion can more efficiently and accurately prioritize functionally related genes compared to shortest path length methods.
  • To assess the method's ability to differentiate genes within biological pathways and those linked to human clinical symptoms.

Main Methods:

  • Comparison of graph-based information diffusion with non-weighted shortest path length approaches.
  • Utilizing biological network information, including edge confidence weights.
  • Statistical analysis to assess the significance of gene prioritization for pathways and clinical symptoms.

Main Results:

  • Graph-based information diffusion significantly differentiated genes in the same biological pathways (p << 0.0001).
  • The method also effectively distinguished genes associated with human drug-induced clinical symptoms (p << 0.0001).
  • Information diffusion prioritized functionally related genes faster and more accurately than shortest path methods (pathways: p = 2.7e-28, clinical symptoms: p = 0.032).

Conclusions:

  • Graph-based information diffusion provides a robust and efficient method for prioritizing functionally related genes.
  • This approach facilitates effective biological network validation and hypothesis generation, particularly for human phenotype-specific genes.