1. Predicting Influenza A Virus Infection in the Lung from Hematological Data with Machine Learning
- Author
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Suneet Singh, Jhutty, Julia D, Boehme, Andreas, Jeron, Julia, Volckmar, Kristin, Schultz, Jens, Schreiber, Klaus, Schughart, Kai, Zhou, Jan, Steinheimer, Horst, Stöcker, Sabine, Stegemann-Koniszewski, Dunja, Bruder, and Esteban A, Hernandez-Vargas
- Subjects
Physiology ,Biochemistry ,Microbiology ,Computer Science Applications ,Machine Learning ,Mice ,Interferon-gamma ,Orthomyxoviridae Infections ,Influenza A virus ,Modeling and Simulation ,Influenza, Human ,Genetics ,Animals ,Humans ,Cytokines ,Lung ,Respiratory Tract Infections ,Molecular Biology ,Ecology, Evolution, Behavior and Systematics - Abstract
The tracking of pathogen burden and host responses with minimally invasive methods during respiratory infections is central for monitoring disease development and guiding treatment decisions. Utilizing a standardized murine model of respiratory influenza A virus (IAV) infection, we developed and tested different supervised machine learning models to predict viral burden and immune response markers, i.e., cytokines and leukocytes in the lung, from hematological data. We performed independently
- Published
- 2022
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