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Classifying Big DNA Methylation Data: A Gene-Oriented Approach

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Database and Expert Systems Applications (DEXA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 903))

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Abstract

Thanks to Next Generation Sequencing (NGS) techniques, public available genomic data of cancer is growing quickly. Indeed, the largest public database of cancer called The Cancer Genome Atlas (TCGA) contains huge amounts of biomedical big data to be analyzed with advanced knowledge extraction methods. In this work, we focus on the NGS experiment of DNA methylation, whose data matrices are composed of hundred thousands of features (i.e., methylated sites). We propose an efficient data processing procedure that permits to obtain a gene-oriented organization and enables to perform a supervised machine learning analysis with state-of-the-art methods. The procedure divides the original data matrices into several sub-matrices, each one containing the sites located within the same gene. We extract from TCGA DNA methylation data of three tumor types (i.e., breast, prostate, and thyroid carcinomas) and we are able to successfully discriminate tumoral from non tumoral samples using function-, tree-, and rule-based classifiers. Finally, we select the best performing genes (matrices) ranking them according to the accuracy of the classifiers and we execute an enrichment analysis of them. Those genes can be further investigated by domain experts for proving their relation to the cancers under study.

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Author information

Authors and Affiliations

  1. Department of Engineering, Uninettuno University, Corso Vittorio Emanuele II 39, 00186, Rome, Italy

    Emanuel Weitschek

  2. Department of Engineering, Roma Tre University, Via della Vasca Navale 79, 00146, Rome, Italy

    Fabio Cumbo & Eleonora Cappelli

  3. Institute for Systems Analysis and Computer Science, National Research Council, Via dei Taurini 19, 00185, Rome, Italy

    Emanuel Weitschek, Fabio Cumbo, Eleonora Cappelli, Giovanni Felici & Paola Bertolazzi

Authors
  1. Emanuel Weitschek
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  2. Fabio Cumbo
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  3. Eleonora Cappelli
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  4. Giovanni Felici
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  5. Paola Bertolazzi
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Corresponding author

Correspondence to Emanuel Weitschek .

Editor information

Editors and Affiliations

  1. University of Tunis, Tunis, Tunisia

    Mourad Elloumi

  2. MiCS, Media Computer Science, University of Passau, Passau, Bayern, Germany

    Michael Granitzer

  3. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  4. University of Twente, Enschede, Overijssel, The Netherlands

    Christin Seifert

  5. Fak. Medien, Bauhaus Universität Weimar, Weimar, Thüringen, Germany

    Benno Stein

  6. Inst. für Softwaretechnik, Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  7. FAW, Johannes Kepler University of Linz, Linz, Austria

    Roland Wagner

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Weitschek, E., Cumbo, F., Cappelli, E., Felici, G., Bertolazzi, P. (2018). Classifying Big DNA Methylation Data: A Gene-Oriented Approach. In: Elloumi, M., et al. Database and Expert Systems Applications. DEXA 2018. Communications in Computer and Information Science, vol 903. Springer, Cham. https://doi.org/10.1007/978-3-319-99133-7_11

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  • DOI: https://doi.org/10.1007/978-3-319-99133-7_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99132-0

  • Online ISBN: 978-3-319-99133-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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