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Updated: Oct 5, 2025

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

Published on: July 18, 2013

Predicting missing links in global host-parasite networks.

Maxwell J Farrell^1,2,3, Mohamad Elmasri⁴, David A Stephens⁴

¹Department of Biology, McGill University, QC, Canada.

The Journal of Animal Ecology

|January 23, 2022

Summary

This summary is machine-generated.

Predicting unknown host-parasite interactions using network data and host evolution helps identify disease risks. This approach efficiently guides research and enhances global disease surveillance efforts for multi-host parasites.

Keywords:

disease ecology host-parasite interactions infectious diseases macroecology phylogenetics

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Prediction of HIV-1 Coreceptor Usage Tropism by Sequence Analysis using a Genotypic Approach

Prediction of HIV-1 Coreceptor Usage Tropism by Sequence Analysis using a Genotypic Approach

Published on: December 1, 2011

Area of Science:

Ecology
Evolutionary Biology
Parasitology

Background:

Multi-host parasites pose significant global health challenges, yet their complete host range remains largely unknown.
Predicting undocumented host-parasite associations is crucial for understanding disease ecology, host specificity, and disease surveillance.
Existing global species interaction networks are often sparse, limiting accurate link prediction.

Purpose of the Study:

To develop and apply novel methods for predicting missing host-parasite links in global mammal-parasite networks.
To demonstrate how predicted links can efficiently guide data collection and improve network completeness.
To leverage network properties and host evolutionary relationships for accurate link prediction.

Main Methods:

Amalgamated a global mammal-parasite interaction network (>29,000 interactions).
Applied a hierarchical Bayesian approach for link prediction using network structure and scaled phylogenetic distances.
Used predictions to guide targeted literature searches for undocumented interactions.

Main Results:

Successfully predicted missing links in global host-parasite networks, including parasites of humans, domesticated animals, and wildlife.
Identified a combination of previously published but missing interactions and potential new associations.
Validated many top predicted links through targeted literature searches.

Conclusions:

Phylogenetic information is valuable for predicting host-parasite interactions.
Iterated prediction and targeted search efficiently guide the collection of host-parasite interaction data.
This approach supports understanding host specificity evolution and enhances disease surveillance by focusing research on high-probability undocumented interactions.