Divide and conquer – machine-learning integrates mammalian, viral, and network traits to predict unknown virus-mammal associations

Wardeh, Maya ORCID: 0000-0002-2316-5460, Blagrove, Marcus SC ORCID: 0000-0002-7510-167X, Sharkey, Kieran and Baylis, Matthew ORCID: 0000-0003-0335-187X (2020) Divide and conquer – machine-learning integrates mammalian, viral, and network traits to predict unknown virus-mammal associations. bioRxiv.

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Official URL: http://dx.doi.org/10.1101/2020.06.13.150003

Abstract

<h4>ABSTRACT</h4> Our knowledge of viral host ranges remains limited. Completing this picture by identifying unknown hosts of known viruses is an important research aim that can help identify zoonotic and animal-disease risks. Furthermore, such understanding can be used to mitigate against viral spill-over from animal reservoirs into human population. To address this knowledge-gap we apply a divide-and-conquer approach which separates viral, mammalian and network features into three unique perspectives, each predicting associations independently to enhance predictive power. Our approach predicts over 20,000 unknown associations between known viruses and mammalian hosts, suggesting that current knowledge underestimates the number of associations in wild and semi-domesticated mammals by a factor of 4.3, and the average mammalian host-range of viruses by a factor of 3.2. In particular, our results highlight a significant knowledge gap in the wild reservoirs of important zoonotic and domesticated mammals’ viruses: specifically, lyssaviruses, bornaviruses and rotaviruses.

Item Type:	Article
Depositing User:	Symplectic Admin
Date Deposited:	14 Jul 2020 08:42
Last Modified:	18 Jan 2023 23:46
DOI:	10.1101/2020.06.13.150003
Open Access URL:	https://www.biorxiv.org/content/10.1101/2020.06.13...
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3093843