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Publications

Journal Article Ppublish

PubMed: 39921901 DOI: 10.1093/bioinformatics/btaf063

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

Bioinformatics 41(3)

Authors: George I Gavriilidis, Vasileios Vasileiou, Stella Dimitsaki, Georgios Karakatsoulis, Antonis Giannakakis, Georgios A Pavlopoulos, Fotis Psomopoulos

Affiliations

  • Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, GR57001, Greece.
  • Department of Molecular Biology and Genetics, Democritus University of Thrace, Alexandroupolis, GR68100, Greece.
  • University Research Institute of Maternal and Child Health and Precision Medicine, National and Kapodistrian University of Athens, Athens, GR11527, Greece.
  • Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, GR16672, Greece.
  • Center of New Biotechnologies & Precision Medicine, Department of Medicine, School of Health Sciences, National and Kapodistrian University of Athens, Athens, GR11528, Greece.

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.

RESULTS

We introduce APNet, a novel computational pipeline that combines differential activity analysis based on SJARACNe co-expression networks with PASNet, a biologically informed sparse deep learning model, to perform explainable predictions for COVID-19 severity. The APNet driver-pathway network ingests SJARACNe co-regulation and classification weights to aid result interpretation and hypothesis generation. APNet outperforms alternative models in patient classification across three COVID-19 proteomic datasets, identifying predictive drivers and pathways, including some confirmed in single-cell omics and highlighting under-explored biomarker circuitries in COVID-19.

AVAILABILITY AND IMPLEMENTATION

APNet's R, Python scripts, and Cytoscape methodologies are available at https://github.com/BiodataAnalysisGroup/APNet.

MeSH

Cells, Classification, Covid-19, Deep Learning, Proteomics, Learning