Network embedding unveils the hidden interactions in the mammalian virome

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-.

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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
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