Network embedding unveils the hidden interactions in the mammalian virome

Predicting host-virus interactions is fundamentally a network science problem. We develop a method for bipartite network prediction that combines a recommender system (linear filtering) with an imputation algorithm based on low-rank graph embedding. We test this method by applying it to a global dat...

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Autores principales: Poisot, Timothée, Ouellet, Marie-Andrée, Mollentze, Nardus, Farrell, Maxwell J., Becker, Daniel J., Brierley, Liam, Albery, Gregory F., Gibb, Rory J., Seifert, Stephanie N., Carlson, Colin J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10318366/
https://www.ncbi.nlm.nih.gov/pubmed/37409053
http://dx.doi.org/10.1016/j.patter.2023.100738
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author Poisot, Timothée
Ouellet, Marie-Andrée
Mollentze, Nardus
Farrell, Maxwell J.
Becker, Daniel J.
Brierley, Liam
Albery, Gregory F.
Gibb, Rory J.
Seifert, Stephanie N.
Carlson, Colin J.
author_facet Poisot, Timothée
Ouellet, Marie-Andrée
Mollentze, Nardus
Farrell, Maxwell J.
Becker, Daniel J.
Brierley, Liam
Albery, Gregory F.
Gibb, Rory J.
Seifert, Stephanie N.
Carlson, Colin J.
author_sort Poisot, Timothée
collection PubMed
description Predicting host-virus interactions is fundamentally a network science problem. We develop a method for bipartite network prediction that combines a recommender system (linear filtering) with an imputation algorithm based on low-rank graph embedding. We test this method by applying it to a global database of mammal-virus interactions and thus show that it makes biologically plausible predictions that are robust to data biases. We find that the mammalian virome is under-characterized anywhere in the world. We suggest that future virus discovery efforts could prioritize the Amazon Basin (for its unique coevolutionary assemblages) and sub-Saharan Africa (for its poorly characterized zoonotic reservoirs). Graph embedding of the imputed network improves predictions of human infection from viral genome features, providing a shortlist of priorities for laboratory studies and surveillance. Overall, our study indicates that the global structure of the mammal-virus network contains a large amount of information that is recoverable, and this provides new insights into fundamental biology and disease emergence.
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spelling pubmed-103183662023-07-05 Network embedding unveils the hidden interactions in the mammalian virome Poisot, Timothée Ouellet, Marie-Andrée Mollentze, Nardus Farrell, Maxwell J. Becker, Daniel J. Brierley, Liam Albery, Gregory F. Gibb, Rory J. Seifert, Stephanie N. Carlson, Colin J. Patterns (N Y) Article Predicting host-virus interactions is fundamentally a network science problem. We develop a method for bipartite network prediction that combines a recommender system (linear filtering) with an imputation algorithm based on low-rank graph embedding. We test this method by applying it to a global database of mammal-virus interactions and thus show that it makes biologically plausible predictions that are robust to data biases. We find that the mammalian virome is under-characterized anywhere in the world. We suggest that future virus discovery efforts could prioritize the Amazon Basin (for its unique coevolutionary assemblages) and sub-Saharan Africa (for its poorly characterized zoonotic reservoirs). Graph embedding of the imputed network improves predictions of human infection from viral genome features, providing a shortlist of priorities for laboratory studies and surveillance. Overall, our study indicates that the global structure of the mammal-virus network contains a large amount of information that is recoverable, and this provides new insights into fundamental biology and disease emergence. Elsevier 2023-04-24 /pmc/articles/PMC10318366/ /pubmed/37409053 http://dx.doi.org/10.1016/j.patter.2023.100738 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Poisot, Timothée
Ouellet, Marie-Andrée
Mollentze, Nardus
Farrell, Maxwell J.
Becker, Daniel J.
Brierley, Liam
Albery, Gregory F.
Gibb, Rory J.
Seifert, Stephanie N.
Carlson, Colin J.
Network embedding unveils the hidden interactions in the mammalian virome
title Network embedding unveils the hidden interactions in the mammalian virome
title_full Network embedding unveils the hidden interactions in the mammalian virome
title_fullStr Network embedding unveils the hidden interactions in the mammalian virome
title_full_unstemmed Network embedding unveils the hidden interactions in the mammalian virome
title_short Network embedding unveils the hidden interactions in the mammalian virome
title_sort network embedding unveils the hidden interactions in the mammalian virome
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10318366/
https://www.ncbi.nlm.nih.gov/pubmed/37409053
http://dx.doi.org/10.1016/j.patter.2023.100738
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