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Virtual Gene: Using Correlations Between Genes to Select Informative Genes on Microarray Datasets

  • Xian Xu
  • Aidong Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3680)

Abstract

Gene Selection is one class of most used data analysis algorithms on microarray datasets. The goal of gene selection algorithms is to filter out a small set of informative genes that best explains experimental variations. Traditional gene selection algorithms are mostly single-gene based. Some discriminative scores are calculated and sorted for each gene. Top ranked genes are then selected as informative genes for further study. Such algorithms ignore completely correlations between genes, although such correlations is widely known. Genes interact with each other through various pathways and regulative networks. In this paper, we propose to use, instead of ignoring, such correlations for gene selection. Experiments performed on three public available datasets show promising results.

Keywords

Feature Selection Prediction Accuracy Gene Pair Gene Selection Feature Selection 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 2005

Authors and Affiliations

  • Xian Xu
    • 1
  • Aidong Zhang
    • 1
  1. 1.Department of Computer Science and EngineeringState University of New York at BuffaloBuffaloUSA

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