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Are filter methods very effective in gene selection of microarray data?

  • Zhou Jun Li*
  • , Li Juan Zhang
  • , Huo Wang Chen
  • *Corresponding author for this work
  • National University of Defense Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Feature (Gene) selection is a frequently used preprocessing technology for successful cancer classification task in microarray gene expression data analysis. Widely used gene selection approaches are mainly focused on the filter methods. Filter methods are usually considered to be very effective and efficient for high-dimensional data. This paper reviews the existing filter methods, and shows the performance of the representative algorithms on microarray data by extensive experimental study. Surprisingly, the experimental results show that filter methods are not very effective on microarray data. We analyze the cause of the result and provide the basic ideas for potential solutions.

Original languageEnglish
Title of host publicationProceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages97-100
Number of pages4
ISBN (Print)9781424416042
DOIs
StatePublished - 2007
Event2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW - San Jose, CA, United States
Duration: 2 Nov 20074 Nov 2007

Publication series

NameProceedings - 2007 IEEE International Conference on Bioinformaticsand Biomedicine Workshops, BIBMW

Conference

Conference2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW
Country/TerritoryUnited States
CitySan Jose, CA
Period2/11/074/11/07

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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