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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/8457| APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19 | |
| George Gavriilidis Vasileios Vasileiou Stella Dimitsaki GEORGIOS KARAKATSOULIS Antonis Giannakakis Georgios Pavlopoulos Fotis Psomopoulos | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://doi.org/10.1101/2024.01.11.575161 | |
| https://www.biorxiv.org/content/10.1101/2024.01.11.575161v1 | |
| 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. | |
| bioRxiv | |
| 11-01-2024 | |
| Preimpreso | |
| Inglés | |
| Público en general | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Materiales de Consulta y Comunicados Técnicos |
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| Fichero | Tamaño | Formato | |
|---|---|---|---|
| APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19.pdf | 3.62 MB | Adobe PDF | Visualizar/Abrir |