Instruct: Identify the main category of Biorxiv papers based on the titles and abstracts
Query: Predicting missing links in global host-parasite networks O_LIParasites that infect multiple species cause major health burdens globally, but for many, the full suite of susceptible hosts is unknown. Predicting undocumented host-parasite associations will help expand knowledge of parasite host specificities, promote the development of theory in disease ecology and evolution, and support surveillance of multi-host infectious diseases. Analysis of global species interaction networks allows for leveraging of information across taxa, but link prediction at this scale is often limited by extreme network sparsity, and lack of comparable trait data across species.
C_LIO_LIHere we use recently developed methods to predict missing links in global mammal-parasite networks using readily available data: network properties and evolutionary relationships among hosts. We demonstrate how these link predictions can efficiently guide the collection of species interaction data and increase the completeness of global species interaction networks.
C_LIO_LIWe amalgamate a global mammal host-parasite interaction network (>29,000 interactions) and apply a hierarchical Bayesian approach for link prediction that leverages information on network structure and scaled phylogenetic distances among hosts. We use these predictions to guide targeted literature searches of the most likely yet undocumented interactions, and identify empirical evidence supporting many of the top "missing" links.
C_LIO_LIWe find that link prediction in global host-parasite networks can accurately predict parasites of humans, domesticated animals, and endangered wildlife, representing a combination of published interactions missing from existing global databases, and potential but currently undocumented associations.
C_LIO_LIOur study provides further insight into the use of phylogenies for predicting host-parasite interactions, and highlights the utility of iterated prediction and targeted search to efficiently guide the collection of host-parasite interaction. These data are critical for understanding the evolution of host specificity, and may be used to support disease surveillance through a process of predicting missing links, and targeting research towards the most likely undocumented interactions.
C_LI
23
The categories of this paper are: immunology
20
The categories of this paper are: pharmacology
22
The categories of this paper are: physiology
32
The categories of this paper are: zoology
28
The categories of this paper are: communication
9
The categories of this paper are: synthetic
12
The categories of this paper are: behavior
19
The categories of this paper are: epidemiology
16
The categories of this paper are: microbiology
1
The categories of this paper are: bioinformatics
17
The categories of this paper are: molecular
8
The categories of this paper are: genomics
3
The categories of this paper are: evolutionary
5
The categories of this paper are: cell
13
The categories of this paper are: animal
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The categories of this paper are: biophysics