APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19.

Journal: Bioinformatics (Oxford, England)
Published Date: Mar 4, 2025

Abstract

MOTIVATION: Computational analyses of bulk and single-cell omics provide translational insights into complex diseases, such as COVID-19, by revealing molecules, cellular phenotypes, and signalling patterns that contribute to unfavourable clinical outcomes. Current in silico approaches dovetail differential abundance, biostatistics, and machine learning, but often overlook nonlinear proteomic dynamics, like post-translational modifications, and provide limited biological interpretability beyond feature ranking.

Authors

  • George I Gavriilidis
    Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thermi, Thessaloniki, Greece.
  • Vasileios Vasileiou
    Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, GR57001, Greece.
  • Stella Dimitsaki
    Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thermi, Thessaloniki, Greece. Electronic address: sdimitsaki@certh.gr.
  • Georgios Karakatsoulis
    Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, GR57001, Greece.
  • Antonis Giannakakis
    Department of Molecular Biology and Genetics, Democritus University of Thrace, Alexandroupolis, GR68100, Greece.
  • Georgios A Pavlopoulos
    Department of Energy, Joint Genome Institute, Walnut Creek, California, USA.
  • Fotis Psomopoulos
    Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki 570 01, Greece.

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