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SemBiosphere: a semantic web approach to recommending microarray clustering services.

Kevin Y Yip1, Peishen Qi, Martin Schultz

  • 1Computer Science, Yale University, New Haven, Connecticut, USA,

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|November 11, 2006
PubMed
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SemBiosphere recommends suitable clustering algorithms for microarray data analysis. This system uses an ontology to match user needs with algorithm properties, simplifying data analysis.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Mining

Background:

  • Clustering is vital for analyzing microarray data.
  • Numerous clustering algorithms exist, making selection difficult.
  • Deploying and running these algorithms presents challenges.

Purpose of the Study:

  • To develop a matchmaking system, SemBiosphere, for recommending clustering algorithms.
  • To simplify the process of identifying, accessing, and utilizing appropriate algorithms for microarray data analysis.

Main Methods:

  • Developed an ontology in OWL to describe clustering algorithms and their performance.
  • Implemented a matchmaking system that recommends algorithms based on user requirements and data properties.
  • Utilized XML-based web services for invoking algorithms across distributed platforms.

Related Experiment Videos

Main Results:

  • SemBiosphere successfully recommends clustering algorithms.
  • The system integrates diverse algorithms implemented in various languages and platforms.
  • The ontology enables machines to understand and recommend algorithms.

Conclusions:

  • SemBiosphere addresses the challenges of selecting and accessing clustering algorithms for microarray data.
  • The ontology-driven approach facilitates automated, intelligent algorithm recommendation.
  • The system enhances the usability and accessibility of clustering tools in bioinformatics.