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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,...
Pleiotropy01:33

Pleiotropy

Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
Epistasis Analysis01:09

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...

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Related Experiment Video

Updated: Jun 18, 2026

Mapping Dysfunctional Protein-Protein Interactions in Disease
09:39

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

Identification of coordinately dysregulated subnetworks in complex phenotypes.

Salim A Chowdhury1, Mehmet Koyutürk

  • 1Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, USA. sxc426@eecs.case.edu

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|November 13, 2009
PubMed
Summary

Identifying coordinately dysregulated subnetworks in protein-protein interaction (PPI) networks improves cancer diagnosis and prognosis. Our NETCOVER algorithm effectively identifies these subnetworks, offering new insights into complex phenotype network dynamics.

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Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Complex phenotypes are difficult to understand using single gene markers.
  • Protein-protein interaction (PPI) networks offer a way to identify multiple interacting markers.
  • Coordinately dysregulated subnetworks show promise in improving cancer diagnosis and prognosis.

Purpose of the Study:

  • To address the algorithmic challenges in identifying coordinately dysregulated subnetworks.
  • To formulate coordinate dysregulation within the framework of the set-cover problem.
  • To adapt set-cover approximation algorithms for identifying these subnetworks.

Main Methods:

  • Formulation of coordinate dysregulation as a set-cover problem.
  • Adaptation of state-of-the-art set-cover approximation algorithms.
  • Application of the NETCOVER algorithm to human colorectal cancer (CRC) data.

Main Results:

  • The NETCOVER algorithm significantly improves cancer diagnosis and metastasis prediction compared to existing methods.
  • Identified subnetworks near known CRC driver genes exhibit significant coordinate dysregulation.
  • Demonstrated the utility of coordinate dysregulation in understanding complex phenotype network dynamics.

Conclusions:

  • The NETCOVER algorithm provides an effective approach for identifying coordinately dysregulated subnetworks.
  • Coordinate dysregulation is a valuable concept for understanding the network dynamics of complex phenotypes like cancer.
  • This approach enhances diagnostic and prognostic capabilities in cancer research.