Predicting Influenza A Virus Infection in the Lung from Hematological Data with Machine Learning-Reference-Cited by-同舟云学术

Predicting Influenza A Virus Infection in the Lung from Hematological Data with Machine Learning

Published:2022-12-20 Issue:6 Volume:7 Page:
ISSN:2379-5077
Container-title:mSystems
language:en
Short-container-title:mSystems

Author:

Jhutty Suneet Singh¹²,Boehme Julia D.³⁴,Jeron Andreas³⁴,Volckmar Julia³⁴,Schultz Kristin³⁵,Schreiber Jens⁶,Schughart Klaus⁵⁷⁸,Zhou Kai¹,Steinheimer Jan¹,Stöcker Horst¹⁹¹⁰,Stegemann-Koniszewski Sabine⁶,Bruder Dunja³⁴,Hernandez-Vargas Esteban A.¹¹¹¹²

Affiliation:

1. Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany

2. Faculty of Biological Sciences, Goethe University, Frankfurt am Main, Germany

3. Immune Regulation Group, Helmholtz Centre for Infection Research, Braunschweig, Germany

4. Infection Immunology Group, Institute of Medical Microbiology, Infection Control and Prevention, Health Campus Immunology, Infectiology and Inflammation, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany

5. Department of Infection Genetics, Helmholtz Centre for Infection Research, Braunschweig, Germany

6. Department of Pneumology, Health Campus Immunology, Infectiology and Inflammation, Otto-von-Guericke University Magdeburg, Magdeburg, Germany

7. Department of Microbiology, Immunology, and Biochemistry, University of Tennessee Health Science Center, Memphis, Tennessee, USA

8. University of Veterinary Medicine Hannover, Hannover, Germany

9. Institut für Theoretische Physik, Goethe Universität Frankfurt, Frankfurt am Main, Germany

10. GSI Helmholtzzentrum für Schwerionenforschung GmbH, Darmstadt, Germany

11. Department of Mathematics and Statistical Science, University of Idaho, Moscow, Idaho, USA

12. Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, USA

Abstract

During the course of respiratory infections such as influenza, we do have a very limited view of immunological indicators to objectively and quantitatively evaluate the outcome of a host. Methods for monitoring immunological markers in a host’s lungs are invasive and expensive, and some of them are not feasible to perform.

Funder

Deutsche Forschungsgemeinschaft

Publisher

American Society for Microbiology

Subject

Computer Science Applications,Genetics,Molecular Biology,Modeling and Simulation,Ecology, Evolution, Behavior and Systematics,Biochemistry,Physiology,Microbiology

Link

https://journals.asm.org/doi/pdf/10.1128/msystems.00459-22

Reference55 articles.

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