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From Omics Data to Candidate Genes: An Innovative Machine Learning Approach for Biomarker Identification

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Artificial Intelligence and Soft Computing (ICAISC 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 15166))

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Abstract

Advances in high-throughput technologies have accelerated omics data research. The recent explosion of omics, namely transcriptomic data, has opened new opportunities for the discovery of novel biomarkers with potential to be incorporated into clinical practice. However, due to their extreme complexity, gaining useful insights is particularly challenging. Hence, the application of machine learning techniques on transcriptomic data emerges as a highly promising area for the discovery of new biomarkers. For exploring the potential of these techniques, this paper proposes a novel approach to process gene expression data with the aim of finding candidate gene signatures. Our methodology consists of an ensemble feature selection strategy based on the Boruta, SVM-RFE and LASSO methods, complemented by a second feature selection based on the gene importance calculated by the Random Forest, XGBoost, Support Vector Machine, Logistic Regression and AdaBoost methods. Performing simulations with a dataset of atopic dermatitis patients, our proposal resulted in an 8-gene signature with high AUC (0.839) and accuracy (0.8462) values.

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Acknowledgments

This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, and the PhD grant: 2022.12728.BD.

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Authors and Affiliations

  1. Algoritmi R&D Centre/LASI, University of Minho, Campus of Gualtar, 4710-057, Braga, Portugal

    Ana Duarte & Orlando Belo

Authors
  1. Ana Duarte
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  2. Orlando Belo
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Corresponding author

Correspondence to Ana Duarte .

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Editors and Affiliations

  1. Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Krakow, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Duarte, A., Belo, O. (2025). From Omics Data to Candidate Genes: An Innovative Machine Learning Approach for Biomarker Identification. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2024. Lecture Notes in Computer Science(), vol 15166. Springer, Cham. https://doi.org/10.1007/978-3-031-81596-6_24

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  • DOI: https://doi.org/10.1007/978-3-031-81596-6_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-81595-9

  • Online ISBN: 978-3-031-81596-6

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