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International Conference on Machine Learning, Optimization, and Data Science

LOD 2020: Machine Learning, Optimization, and Data Science pp 125–136Cite as

Sparse Consensus Classification for Discovering Novel Biomarkers in Rheumatoid Arthritis

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12565))

Abstract

Rheumatoid arthritis (RA) is a long-term autoimmune disease that severely affects physical function and quality of life. Patients diagnosed with RA are usually treated with anti-tumor necrosis factor (anti-TNF), which in certain cases do not contribute to reach remission. Consequently, there is a need to develop models that can predict therapy response, thus preventing disability, maintain life quality, and decrease cost treatment. Transcriptomic data are emerging as valuable information to predict RA pathogenesis and therapy outcome. The aim of this study is to find gene signatures in RA patients that help to predict the response to anti-TNF treatment. RNA-sequencing of whole blood samples dataset from RA patients at baseline and following 3 months of therapy were used. A methodology based on sparse logistic regression was employed to obtain predictive models which allowed to find 20 genes consensually associated with therapy response, some known to be related with RA. Gene expression levels at 3 months of therapy showed no added value in the prediction of response to therapy when compared with the baseline. The analysis using Bayesian network learning unveiled significant protein-protein interactions in both good and non-responders, further confirmed using the STRING database. Structured sparse regression coupled with Bayesian learning can support the identification of disease biomarkers and generate hypotheses to be further analysed by clinicians.

Partially funded by FCT (PTDC/CCI-CIF/29877/2017, UIDB/50021/2020, UIDB/50008/2020).

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Author information

Authors and Affiliations

  1. INESC-ID, Instituto Superior Técnico, ULisboa, Lisbon, Portugal

    Cláudia Constantino & Susana Vinga

  2. Instituto de Telecomunicações, Instituto Superior Técnico, ULisboa, Lisbon, Portugal

    Alexandra M. Carvalho

Authors
  1. Cláudia Constantino
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  2. Alexandra M. Carvalho
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  3. Susana Vinga
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Corresponding author

Correspondence to Susana Vinga .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Giorgio Jansen

  5. Almawave, Rome, Italy

    Vincenzo Sciacca

  6. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Harvard University, Cambridge, MA, USA

    Renato Umeton

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Constantino, C., Carvalho, A.M., Vinga, S. (2020). Sparse Consensus Classification for Discovering Novel Biomarkers in Rheumatoid Arthritis. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12565. Springer, Cham. https://doi.org/10.1007/978-3-030-64583-0_13

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  • DOI: https://doi.org/10.1007/978-3-030-64583-0_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64582-3

  • Online ISBN: 978-3-030-64583-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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