Digital phagograms : predicting phage infectivity through a multilayer machine learning approach
- Author
- Cédric Lood, Dimitri Boeckaerts (UGent) , Michiel Stock (UGent) , Bernard De Baets (UGent) , Rob Lavigne, Vera van Noort and Yves Briers (UGent)
- Organization
- Project
- Abstract
- Machine learning has been broadly implemented to investigate biological systems. In this regard, the field of phage biology has embraced machine learning to elucidate and predict phage- host interactions, based on receptor-binding proteins, (anti-) defense systems, prophage detection, and life cycle recognition. Here, we highlight the enormous potential of integrating information from omics data with insights from systems biology to better understand phage-host interactions. We conceptualize and discuss the potential of a multilayer model that mirrors the phage infection process, integrating adsorption, bacterial pan-immune components and hijacking of the bacterial metabolism to predict phage infectivity. In the future, this model can offer insights into the underlying mechanisms of the infection process, and digital phagograms can support phage cocktail design and phage engineering.
- Keywords
- Virology, HOST-RANGE, CHALLENGES, VIRULENCE, EVOLUTION, FUTURE
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8733425
- MLA
- Lood, Cédric, et al. “Digital Phagograms : Predicting Phage Infectivity through a Multilayer Machine Learning Approach.” CURRENT OPINION IN VIROLOGY, vol. 52, 2022, pp. 174–81, doi:10.1016/j.coviro.2021.12.004.
- APA
- Lood, C., Boeckaerts, D., Stock, M., De Baets, B., Lavigne, R., van Noort, V., & Briers, Y. (2022). Digital phagograms : predicting phage infectivity through a multilayer machine learning approach. CURRENT OPINION IN VIROLOGY, 52, 174–181. https://doi.org/10.1016/j.coviro.2021.12.004
- Chicago author-date
- Lood, Cédric, Dimitri Boeckaerts, Michiel Stock, Bernard De Baets, Rob Lavigne, Vera van Noort, and Yves Briers. 2022. “Digital Phagograms : Predicting Phage Infectivity through a Multilayer Machine Learning Approach.” CURRENT OPINION IN VIROLOGY 52: 174–81. https://doi.org/10.1016/j.coviro.2021.12.004.
- Chicago author-date (all authors)
- Lood, Cédric, Dimitri Boeckaerts, Michiel Stock, Bernard De Baets, Rob Lavigne, Vera van Noort, and Yves Briers. 2022. “Digital Phagograms : Predicting Phage Infectivity through a Multilayer Machine Learning Approach.” CURRENT OPINION IN VIROLOGY 52: 174–181. doi:10.1016/j.coviro.2021.12.004.
- Vancouver
- 1.Lood C, Boeckaerts D, Stock M, De Baets B, Lavigne R, van Noort V, et al. Digital phagograms : predicting phage infectivity through a multilayer machine learning approach. CURRENT OPINION IN VIROLOGY. 2022;52:174–81.
- IEEE
- [1]C. Lood et al., “Digital phagograms : predicting phage infectivity through a multilayer machine learning approach,” CURRENT OPINION IN VIROLOGY, vol. 52, pp. 174–181, 2022.
@article{8733425,
abstract = {{Machine learning has been broadly implemented to investigate biological systems. In this regard, the field of phage biology has embraced machine learning to elucidate and predict phage- host interactions, based on receptor-binding proteins, (anti-) defense systems, prophage detection, and life cycle recognition. Here, we highlight the enormous potential of integrating information from omics data with insights from systems biology to better understand phage-host interactions. We conceptualize and discuss the potential of a multilayer model that mirrors the phage infection process, integrating adsorption, bacterial pan-immune components and hijacking of the bacterial metabolism to predict phage infectivity. In the future, this model can offer insights into the underlying mechanisms of the infection process, and digital phagograms can support phage cocktail design and phage engineering.}},
author = {{Lood, Cédric and Boeckaerts, Dimitri and Stock, Michiel and De Baets, Bernard and Lavigne, Rob and van Noort, Vera and Briers, Yves}},
issn = {{1879-6257}},
journal = {{CURRENT OPINION IN VIROLOGY}},
keywords = {{Virology,HOST-RANGE,CHALLENGES,VIRULENCE,EVOLUTION,FUTURE}},
language = {{eng}},
pages = {{174--181}},
title = {{Digital phagograms : predicting phage infectivity through a multilayer machine learning approach}},
url = {{http://doi.org/10.1016/j.coviro.2021.12.004}},
volume = {{52}},
year = {{2022}},
}
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