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

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 non-linear 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.

Supplementary information: Supplementary information can be accessed in Zenodo (10.5281/zenodo.14680520).