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Tissue Classification Using Gene Expression Data and Artificial Neural Network Ensembles

  • Huijuan Lu
  • Jinxiang Zhang
  • Lei Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4115)

Abstract

An important challenge in the use of large-scale gene expression data for biological classification occurs when the number of genes far exceeds the number of samples. This situation will make the classification results are unstable. Thus, a tissue classification method using artificial neural network ensembles was proposed. In this method, a feature preselection method is presented to identify significant genes highly correlated with tissue types. Then pseudo data sets for training the component neural network of ensembles were generated by bagging. The predictions of those individual networks were combined by simple averaging method. Some data experiments have shown that this classification method yields competitive results on several publicly available datasets.

Keywords

Artificial Neural Network Gene Expression Data Receiver Operator Characteristic Curve Neural Network Ensemble Tissue Classification 
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

  • Huijuan Lu
    • 1
    • 2
  • Jinxiang Zhang
    • 3
  • Lei Zhang
    • 1
  1. 1.Institute of Computer ApplicationsChina Jiliang UniversityHangzhouChina
  2. 2.College of Computer ScienceZhejiang UniversityHangzhouChina
  3. 3.Department of Computer ScienceZhejiang Education InstituteHangzhouChina

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