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Interpreting Gene Profiles from Biomedical Literature Mining with Self Organizing Maps

  • Shi Yu
  • Steven Van Vooren
  • Bert Coessens
  • Bart De Moor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

We present an approach to interpret gene profiles derived from biomedical literature using Self Organizing Maps (SOMs). Comparison of different clustering algorithms shows that SOMs perform better in grouping high dimensional gene profiles when a lot of noise is present in the data. Qualitative analysis of the clustering results prove that SOMs allow an in-depth interpretation of gene profiles with biological relevance.

Keywords

Cluster Result Biomedical Literature Inverse Document Frequency Automatic Cluster Manual Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shi Yu
    • 1
  • Steven Van Vooren
    • 1
  • Bert Coessens
    • 1
  • Bart De Moor
    • 1
  1. 1.Department of Electrical EngineeringUniversity of LeuvenBelgium

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