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Personalized Modeling Based Gene Selection for Microarray Data Analysis

  • Yingjie Hu
  • Qun Song
  • Nikola Kasabov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5506)

Abstract

This paper presents a novel gene selection method based on personalized modeling. Identifying a compact set of genes from gene expression data is a critical step in bioinformatics research. Personalized modeling is a recently introduced technique for constructing clinical decision support systems. In this paper we have provided a comparative study using the proposed Personalized Modeling based Gene Selection method (PMGS) on two benchmark microarray datasets (Colon cancer and Central Nervous System cancer data). The experimental results show that our method is able to identify a small number of informative genes which can lead to reproducible and acceptable predictive performance without expensive computational cost. These genes are of importance for specific groups of people for cancer diagnosis and prognosis.

Keywords

Gene Selection Clinical Decision Support System Microarray Data Analysis Informative Gene Wrap Method 
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 2009

Authors and Affiliations

  • Yingjie Hu
    • 1
  • Qun Song
    • 1
  • Nikola Kasabov
    • 1
  1. 1.The Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyNew Zealand

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