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Computational Intelligence in Clinical Oncology: Lessons Learned from an Analysis of a Clinical Study

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Computational Intelligence in Biomedicine and Bioinformatics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 151))

Summary

In this chapter, we present a retrospective clinical study where the adoption of computational intelligence approaches for performing knowledge extraction from gene expression data enabled an improved oncological clinical analysis. This study focuses on a survival analysis of estrogen receptor (ER) positive breast cancer patients treated with tamoxifen. The chapter describes each step of the gene expression data analysis procedure, from the quality control of data to the final validation going through normalization, feature transformation, feature selection, and model building. Each section proposes a set of guidelines and motivates the specific choice made for this particular study. Finally, the main guidelines that emerged from this study are the use of simple and effective techniques rather than complex non-linear models, the use of interpretable methods and the use of scalable computational solutions able to deal with multiplatform and multisource data.

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Author information

Authors and Affiliations

  1. Machine Learning Group, Université Libre de Bruxelles, Brussels, Belgium

    B. Haibe-Kains & G. Bontempi

  2. Functional Genomics Unit, Institut Jules Bordet, Brussels, Belgium

    B. Haibe-Kains, C. Desmedt & C. Sotiriou

  3. Peter MacCallum Cancer Center, East Melbourne, Victoria, Australia

    S. Loi

  4. Bioinformatics Core Facility, Institut Suisse de Recherche Expérimentale sur le Cancer, Lausanne, Switzerland

    M. Delorenzi

Authors
  1. B. Haibe-Kains
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  2. C. Desmedt
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  3. S. Loi
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  4. M. Delorenzi
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  5. C. Sotiriou
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  6. G. Bontempi
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Editor information

Editors and Affiliations

  1. Department of Biology, Emory University, 1510 Clifton Rd.NE, 30322, Atlanta, Georgia, USA

    Tomasz G. Smolinski

  2. Department of Computer Science, University of Arkansas at Little Rock, 2801 S.University Ave., 72204, Little Rock, Arkansas, USA

    Mariofanna G. Milanova

  3. Department of Quantitative Methods and Information Systems College of Business and Administration, Kuwait University, P.O. Box 5486, 13055, Safat, Kuwait

    Aboul-Ella Hassanien

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Haibe-Kains, B., Desmedt, C., Loi, S., Delorenzi, M., Sotiriou, C., Bontempi, G. (2008). Computational Intelligence in Clinical Oncology: Lessons Learned from an Analysis of a Clinical Study. In: Smolinski, T.G., Milanova, M.G., Hassanien, AE. (eds) Computational Intelligence in Biomedicine and Bioinformatics. Studies in Computational Intelligence, vol 151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70778-3_10

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  • DOI: https://doi.org/10.1007/978-3-540-70778-3_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70776-9

  • Online ISBN: 978-3-540-70778-3

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