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Knowledge Extraction from Microarray Datasets Using Combined Multiple Models to Predict Leukemia Types

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Data Mining: Foundations and Practice

Part of the book series: Studies in Computational Intelligence ((SCI,volume 118))

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Summary

Recent advances in microarray technology offer the ability to measure expression levels of thousands of genes simultaneously. Analysis of such data helps us identifying different clinical outcomes that are caused by expression of a few predictive genes. This chapter not only aims to select key predictive features for leukemia expression, but also demonstrates the rules that classify differentially expressed leukemia genes. The feature extraction and classification are carried out with combination of the high accuracy of ensemble based algorithms, and comprehensibility of a single decision tree. These allow deriving exact rules by describing gene expression differences among significantly expressed genes in leukemia. It is evident from our results that it is possible to achieve better accuracy in classifying leukemia without sacrificing the level of comprehensibility.

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Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000, Maribor, Slovenia

    Gregor Stiglic & Peter Kokol

  2. School of Computing Science, Middlesex University, The Burrough, Hendon, London, NW4 4BT, UK

    Nawaz Khan

Authors
  1. Gregor Stiglic
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  2. Nawaz Khan
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  3. Peter Kokol
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Editor information

Editors and Affiliations

  1. Department of Computer Science, San Jose State University, San Jose, CA, 95192, USA

    Tsau Young Lin

  2. Department of Computer Science and Information Systems, Kennesaw State University, Building 11, Room 3060 1000 Chastain Road, Kennesaw, GA, 30144, USA

    Ying Xie

  3. Department of Computer Science, The University at Stony Brook, Stony Brook, New York, 11794-4400, USA

    Anita Wasilewska

  4. Institute of Information Science, Academia Sinica, No 128, Academia Road, Section 2 Nankang, Taipei, 11529, Taiwan

    Churn-Jung Liau

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© 2008 Springer-Verlag Berlin Heidelberg

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Stiglic, G., Khan, N., Kokol, P. (2008). Knowledge Extraction from Microarray Datasets Using Combined Multiple Models to Predict Leukemia Types. In: Lin, T.Y., Xie, Y., Wasilewska, A., Liau, CJ. (eds) Data Mining: Foundations and Practice. Studies in Computational Intelligence, vol 118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78488-3_20

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  • DOI: https://doi.org/10.1007/978-3-540-78488-3_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78487-6

  • Online ISBN: 978-3-540-78488-3

  • eBook Packages: EngineeringEngineering (R0)

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