A Deceiving Charm of Feature Selection: The Microarray Case Study

  • Miron B. Kursa
  • Witold R. Rudnicki
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 103)

Abstract

Microarray analysis has become a significant use of machine learning in molecular biology. Datasets obtained from this method consist of tens of thousands of attributes usually describing tens of objects. Such setting makes the use of some form of feature selection an inevitable step of analysis—mostly to reduce the feature set to manageable size, but also to obtain an biological insight in the mechanisms of the investigated process. In this paper we present a reanalysis of a previously published late radiation toxicity prediction problem. On that lurid example we show how futile it may be to rely on non-validated feature selection and how even advanced algorithms fail to distinguish between noise and signal when the latter is weak. We also propose methods of detecting and dealing with mentioned problems.

Keywords

gene expression feature selection random forest 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Miron B. Kursa
    • 1
  • Witold R. Rudnicki
    • 1
  1. 1.Interdisciplinary Centre for Mathematical and Computational ModellingUniversity of WarsawWarsawPoland

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