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

Abstract

Motivation: Computational analyses of plasma proteomics provide translational insights into complex diseases such as COVID-19 by revealing molecules, cellular phenotypes, and signaling patterns that contribute to unfavorable clinical outcomes. Current in silico approaches dovetail differential expression, 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 co-expression 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 R, Python scripts and Cytoscape methodologies are available at https://github.com/BiodataAnalysisGroup/APNet Contact: [email protected] Supplementary information: Supplementary information can be accessed in Zenodo (10.5281/zenodo.10438830).

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

* https://zenodo.org/records/10438830

* https://github.com/BiodataAnalysisGroup/APNet