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iCOSSY: An Online Tool for Context-Specific Subnetwork Discovery from Gene Expression Data.

Ashis Saha1, Minji Jeon1, Aik Choon Tan2

  • 1Department of Computer Science and Engineering, Korea University, Seoul, Korea.

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iCOSSY is a new online tool that helps biologists discover specific molecular subnetworks from gene expression data. This tool aids in understanding the biological basis of different phenotypes by analyzing gene interactions.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Systems biology

Background:

  • Pathway analysis is crucial for understanding complex biological phenotypes.
  • Multiple pathway analyses are often needed due to the lack of a single definitive answer.
  • Implementing diverse algorithms can be inefficient for individual researchers.

Purpose of the Study:

  • To present iCOSSY, an online tool implementing a novel pathway-based Context-specific Subnetwork discovery (COSSY) algorithm.
  • To provide researchers with an efficient and accessible platform for gene expression data analysis.
  • To enhance the reliability and interpretability of pathway analysis through modified COSSY algorithms.

Main Methods:

  • Development of the iCOSSY web server.
  • Implementation of the COSSY algorithm with modifications for improved performance.
  • User-friendly interface for uploading gene expression datasets and defining phenotypes.
  • Interactive visualization of discovered subnetworks.

Main Results:

  • iCOSSY successfully identifies context-specific subnetworks from gene expression data.
  • The tool facilitates the differentiation between two phenotypes based on molecular mechanisms.
  • Interactive visualization aids in understanding the biological significance of identified subnetworks.

Conclusions:

  • iCOSSY offers an efficient solution for complex pathway analysis in biological research.
  • The tool empowers researchers to explore gene expression data and uncover phenotype-specific molecular interactions.
  • iCOSSY enhances the accessibility and interpretability of subnetwork discovery for a wider scientific community.