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

Abstract

AbstractMotivationComputational 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. Currentin silicoapproaches 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.ResultsWe 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 ImplementationAPNet’s R, Python scripts and Cytoscape methodologies are available athttps://github.com/BiodataAnalysisGroup/APNetContactggeorav@certh.grSupplementary informationSupplementary information can be accessed in Zenodo (10.5281/zenodo.10438830).