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TECD: A Transformer Encoder Convolutional Decoder for High-Dimensional Biomedical Data

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Computational Science and Its Applications – ICCSA 2023 Workshops (ICCSA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14104))

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Abstract

In recent years, machine learning and deep learning methods have been increasingly explored in a variety of application domains, including healthcare. Despite the rapid advances in this field, several challenges still need to be addressed to properly model complex biomedical datasets, such as genomic datasets or physiological signals from wearable sensors, that exhibit a very high dimensionality, i.e., a high number of variables or features which can be mutually related. As evidenced by the literature, the induction of reliable predictive models becomes intrinsically harder as the data dimensionality increases. To give a contribution to this field, this paper explores a new deep learning approach that leverages the emerging paradigm of Transformers, which can capture long-range dependencies among the input features and combines them with a Convolutional Neural Network, which is suited for capturing local patterns and dependencies. The resulting architecture has shown very promising results on six biomedical datasets with high dimensionality (several thousands of features), paving the way for further research in this area.

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Notes

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Acknowledgements

This research was supported by the ASTRID project (Fondazione di Sardegna, L.R. 7 agosto 2007, n\(^{\circ }\)7, CUP: F75F21001220007).

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Cagliari, via Ospedale 72, 09124, Cagliari, Italy

    Luca Zedda, Alessandra Perniciano, Andrea Loddo & Barbara Pes

Authors
  1. Luca Zedda
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  2. Alessandra Perniciano
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  3. Andrea Loddo
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  4. Barbara Pes
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Corresponding author

Correspondence to Andrea Loddo .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Basilicata, Potenza, Italy

    Francesco Scorza

  6. University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

  7. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

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Zedda, L., Perniciano, A., Loddo, A., Pes, B. (2023). TECD: A Transformer Encoder Convolutional Decoder for High-Dimensional Biomedical Data. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14104. Springer, Cham. https://doi.org/10.1007/978-3-031-37105-9_16

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  • DOI: https://doi.org/10.1007/978-3-031-37105-9_16

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