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A gene expression analysis system for medical diagnosis

  • Dimitris Maroulis
  • Dimitris Iakovidis
  • Ilias Flaounas
  • Stavros Karkanis
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 204)

Abstract

In this paper we present a novel system that utilizes molecular-level information for medical diagnosis. It accepts high dimensional vectors of gene expressions, quantified by means of microarray image analysis, as input. The proposed system incorporates various data pre-processing methods, such as missing values estimation and data normalization. A novel approach to the classification of gene expression vectors in multiple classes that embodies vari-ous gene selection methods has been adopted for diagnostic purposes. The pro-posed system has been extensively tested on various, publicly available data-sets. We demonstrate its performance for prostate cancer diagnosis and corn-pare its performance with a well established multiclass classification scheme. The results show that the proposed system could be proved a valuable diagnostic aid in medicine.

Keywords

Gene Expression Data Prostate Cancer Diagnosis Classification Error Rate Gene Expression Matrix Gene Expression Vector 
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

© International Federation for Information Processing 2006

Authors and Affiliations

  • Dimitris Maroulis
    • 1
  • Dimitris Iakovidis
    • 1
  • Ilias Flaounas
    • 1
  • Stavros Karkanis
    • 2
  1. 1.Dept. of Informatics and Telecommunications, PanepistimiopolisUniversity of AthensIlisiaGreece
  2. 2.Dept. of Informatics and Computer TechnologyLamia Institute of TechnologyLamiaGreece

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