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

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. Co-expression and classification weights are ingested by the APNet driver-pathway network 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 by 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