Poisot, Timothee, Ouellet, Marie-Andree, Mollentze, Nardus, Farrell, Maxwell J, Becker, Daniel J, Brierley, Liam 
ORCID: 0000-0002-3026-4723, Albery, Gregory F, Gibb, Rory J, Seifert, Stephanie N and Carlson, Colin J
(2023)
Network embedding unveils the hidden interactions in the mammalian virome.
PATTERNS, 4 (6).
100738-.
Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | imputation, singular value decomposition, virome, zoonotic viruses |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Population Health |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 18 Aug 2023 10:43 |
| Last Modified: | 18 Aug 2023 10:43 |
| DOI: | 10.1016/j.patter.2023.100738 |
| Open Access URL: | https://www.sciencedirect.com/science/article/pii/... |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3172239 |
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