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

Authors

Georgios Karakatsoulis (ORCID: https://orcid.org/0000-0002-8496-3087)
Vasileios Vasileiou (ORCID: https://orcid.org/0000-0003-0145-033X)
Stella Dimitsaki (ORCID: https://orcid.org/0000-0002-6356-9531)
George Gavriilidis (ORCID: https://orcid.org/0000-0003-2575-4354)
Antonis Giannakakis (ORCID: https://orcid.org/0000-0003-2975-0326)
Fotis Psomopoulos (ORCID: https://orcid.org/0000-0002-0222-4273)
Georgios A. Pavlopoulos (ORCID: https://orcid.org/0000-0002-4577-8276)

Institutions

Democritus University of Thrace (GR)
National and Kapodistrian University of Athens (GR)
Centre for Research and Technology Hellas (GR)
Alexander Fleming Biomedical Sciences Research Center (GR)

Publication Details

Journal: Bioinformatics
Published: 2025-02-05
DOI: https://doi.org/10.1093/bioinformatics/btaf063
Citations: 4
Primary Topic: SARS-CoV-2 and COVID-19 Research
Type: article
Field-Weighted Citation Impact: 5.58

Funders

HORIZON EUROPE Framework Programme

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article

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

Georgios Karakatsoulis, Vasileios Vasileiou, Stella Dimitsaki, George Gavriilidis et al.

Feb 5, 2025

4 citations

Bioinformatics

SARS-CoV-2 and COVID-19 Research

5.58

article

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

Georgios Karakatsoulis, Vasileios Vasileiou, Stella Dimitsaki, George Gavriilidis, Antonis Giannakakis, Fotis Psomopoulos, Georgios A. Pavlopoulos

February 5, 2025article en

4 citations

Abstract

No abstract available for this paper.

BioinformaticsVol. 41(3)

Democritus University of Thrace (GR), National and Kapodistrian University of Athens (GR), Centre for Research and Technology Hellas (GR), Alexander Fleming Biomedical Sciences Research Center (GR)

HORIZON EUROPE Framework Programme

Openalex Percentile: Top 5%

SARS-CoV-2 and COVID-19 Research

5.58

DOI Open Access PDF OpenAlex

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