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Supporting Clinico-Genomic Knowledge Discovery: A Multi-strategy Data Mining Process

  • Alexandros Kanterakis
  • George Potamias
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3955)

Abstract

We present a combined clinico-genomic knowledge discovery (CGKD) process suited for linking gene-expression (microarray) and clinical patient data. The process present a multi-strategy mining approach realized by the smooth integration of three distinct data-mining components: clustering (based on a discretized k-means approach), association rules mining, and feature-selection for selecting discrimant genes. The proposed CGKD process is applied on a real-world gene-expression profiling study (i.e., clinical outcome of breast cancer patients). Assessment of the results demonstrates the rationality and reliability of the approach.

Keywords

Association Rule Association Rule Mining Clinical Attribute Discriminant Gene Clinical Patient Data 
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

  • Alexandros Kanterakis
    • 1
  • George Potamias
    • 1
  1. 1.Institute of Computer ScienceFoundation for Research & Technology – Hellas (FORTH)Heraklion, CreteGreece

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