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International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2020: Intelligent Data Engineering and Automated Learning – IDEAL 2020 pp 54–65Cite as

Data Generation Using Gene Expression Generator

Part of the Lecture Notes in Computer Science book series (LNISA,volume 12490)

Abstract

Generative adversarial networks (GANs) could be used efficiently for image and video generation when labeled training data is available in bulk. In general, building a good machine learning model requires a reasonable amount of labeled training data. However, there are areas such as the biomedical field where the creation of such a dataset is time-consuming and requires expert knowledge. Thus, the aim is to use data augmentation techniques as an alternative to data collection to improve data classification. This paper presents the use of a modified version of a GAN called Gene Expression Generator (GEG) to augment the available data samples. The proposed approach was used to generate synthetic data for binary biomedical datasets to train existing supervised machine learning approaches. Experimental results show that the use of GEG for data augmentation with a modified version of leave one out cross-validation (LOOCV) increases the performance of classification accuracy.

Keywords

  • Data generation
  • Generative adversarial networks
  • Gene expression data
  • Cancer classification

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Fig. 1.
Fig. 2.
Fig. 3.

Notes

  1. 1.

    x denotes original (positive) instances, \(x'\) denotes synthetic (negative) instances, z is a random Gaussian noise. D(x) and \(D(x')\) are discriminator’s outputs for original and synthetic instances respectively, and G(z) is the generator’s output.

  2. 2.

    DNA microarray data: https://homes.di.unimi.it/~valentini/DATA/MICROAR-RAY-DATA/.

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Acknowledgments

This work was supported by the Telekom Innovation Laboratories (T-Labs) and the Research and Development unit of Deutsche Telekom.

The authors would like to express their deepest gratitude towards Tsegaye Misikir Tashu for his advice, valuable feedback, proofreading and assistance in overcoming technical problems.

Project no. ED_18-1-2019-0030 (Application domain specific highly reliable IT solutions subprogramme) has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the Thematic Excellence Programme funding scheme.

Author information

Authors and Affiliations

  1. Department of Data Science and Engineering, Telekom Innovation Laboratories, Faculty of Informatics, ELTE–Eötvös Loránd University, Pázmány Péter sétány 1/C, 1117, Budapest, Hungary

    Zakarya Farou & Tomáš Horváth

  2. Science and Technology Campus, Bordeaux Computer Science Laboratory-LaBRI, University of Bordeaux, 351 Cours de la Liberátion, 33400, Talence, France

    Noureddine Mouhoub

  3. Institute of Computer Science, Pavol Jozef Šafárik University, Jesenná 5, 040 01, Košice, Slovakia

    Tomáš Horváth

Authors
  1. Zakarya Farou
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  2. Noureddine Mouhoub
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  3. Tomáš Horváth
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Corresponding author

Correspondence to Zakarya Farou .

Editor information

Editors and Affiliations

  1. University of Minho, Braga, Portugal

    Cesar Analide

  2. University of Minho, Braga, Portugal

    Dr. Paulo Novais

  3. Technical University of Madrid, Madrid, Spain

    David Camacho

  4. University of Manchester, Manchester, UK

    Dr. Hujun Yin

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Farou, Z., Mouhoub, N., Horváth, T. (2020). Data Generation Using Gene Expression Generator. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12490. Springer, Cham. https://doi.org/10.1007/978-3-030-62365-4_6

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  • DOI: https://doi.org/10.1007/978-3-030-62365-4_6

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