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Related Experiment Videos

Extracting dynamics from static cancer expression data.

Anupam Gupta1, Ziv Bar-Joseph

  • 1Department of Computer Science, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA. anupang@cs.cmu.edu

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|May 3, 2008
PubMed
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Researchers can now determine the temporal order of static gene expression samples, crucial for understanding disease progression like cancer. This method uses a traveling salesman problem (TSP) approach for accurate temporal reconstruction and identifies key genes.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Static gene expression datasets offer snapshots of disease progression, but lack temporal information.
  • Understanding the dynamics of diseases like cancer requires inferring the order of these snapshots.
  • Current methods struggle to accurately reconstruct the temporal order from static expression data.

Purpose of the Study:

  • To develop a method for inferring the temporal order of static gene expression samples.
  • To demonstrate the feasibility of using the Traveling Salesman Problem (TSP) for temporal ordering.
  • To identify genes associated with disease progression through temporal analysis.

Main Methods:

  • Formally proved that TSP can recover the temporal order of static expression datasets under a specific gene expression dynamics model.

Related Experiment Videos

  • Devised an algorithm combining TSP heuristics and probabilistic modeling for temporal order inference.
  • Constructed probabilistic continuous curves to represent gene expression profiles for temporal reconstruction.

    • Main Results:

    • Successfully recovered the correct temporal ordering of static expression datasets by solving a TSP.
    • The inferred temporal order for cancer expression data correlated well with patient survival duration.
    • A classifier using the derived ordering outperformed existing methods.
    • Identified gene sets enriched for cancer progression markers based on the temporal ordering.

    Conclusions:

    • It is possible to determine the temporal order of static expression data using TSP and probabilistic modeling.
    • This approach enhances understanding of disease dynamics and aids in identifying progression-related genes.
    • The method shows promise for improving cancer subtyping and prognosis prediction.