tetano
Editor, Senior Moderator
Bioinform Adv
. 2025 Feb 5;5(1):vbaf016.
doi: 10.1093/bioadv/vbaf016. eCollection 2025. Understanding ecological systems using knowledge graphs: an application to highly pathogenic avian influenza
Hailey Robertson[SUP] 1 2 [/SUP], Barbara A Han[SUP] 3 [/SUP], Adrian A Castellanos[SUP] 3 [/SUP], David Rosado[SUP] 2 [/SUP], Guppy Stott[SUP] 4 5 6 [/SUP], Ryan Zimmerman[SUP] 2 [/SUP], John M Drake[SUP] 5 6 [/SUP], Ellie Graeden[SUP] 2 7 [/SUP]
Affiliations
Motivation: Ecological systems are complex. Representing heterogeneous knowledge about ecological systems is a pervasive challenge because data are generated from many subdisciplines, exist in disparate sources, and only capture a subset of interactions underpinning system dynamics. Knowledge graphs (KGs) have been successfully applied to organize heterogeneous data and to predict new linkages in complex systems. Though not previously applied broadly in ecology, KGs have much to offer in an era when system dynamics are responding to rapid changes across multiple scales.
Results: We developed a KG to demonstrate the method's utility for ecological problems focused on highly pathogenic avian influenza (HPAI), a highly transmissible virus with a broad host range, wide geographic distribution, and rapid evolution with pandemic potential. We describe the development of a graph to include data related to HPAI including pathogen-host associations, species distributions, and population demographics, using a semantic ontology that defines relationships within and between datasets. We use the graph to perform a set of proof-of-concept analyses validating the method and identifying patterns of HPAI ecology. We underscore the generalizable value of KGs to ecology including ability to reveal previously known relationships and testable hypotheses in support of a deeper mechanistic understanding of ecological systems.
Availability and implementation: The data and code are available under the MIT License on GitHub at https://github.com/cghss-data-lab/uga-pipp.
. 2025 Feb 5;5(1):vbaf016.
doi: 10.1093/bioadv/vbaf016. eCollection 2025. Understanding ecological systems using knowledge graphs: an application to highly pathogenic avian influenza
Hailey Robertson[SUP] 1 2 [/SUP], Barbara A Han[SUP] 3 [/SUP], Adrian A Castellanos[SUP] 3 [/SUP], David Rosado[SUP] 2 [/SUP], Guppy Stott[SUP] 4 5 6 [/SUP], Ryan Zimmerman[SUP] 2 [/SUP], John M Drake[SUP] 5 6 [/SUP], Ellie Graeden[SUP] 2 7 [/SUP]
Affiliations
- PMID: 40041112
- PMCID: PMC11879169
- DOI: 10.1093/bioadv/vbaf016
Motivation: Ecological systems are complex. Representing heterogeneous knowledge about ecological systems is a pervasive challenge because data are generated from many subdisciplines, exist in disparate sources, and only capture a subset of interactions underpinning system dynamics. Knowledge graphs (KGs) have been successfully applied to organize heterogeneous data and to predict new linkages in complex systems. Though not previously applied broadly in ecology, KGs have much to offer in an era when system dynamics are responding to rapid changes across multiple scales.
Results: We developed a KG to demonstrate the method's utility for ecological problems focused on highly pathogenic avian influenza (HPAI), a highly transmissible virus with a broad host range, wide geographic distribution, and rapid evolution with pandemic potential. We describe the development of a graph to include data related to HPAI including pathogen-host associations, species distributions, and population demographics, using a semantic ontology that defines relationships within and between datasets. We use the graph to perform a set of proof-of-concept analyses validating the method and identifying patterns of HPAI ecology. We underscore the generalizable value of KGs to ecology including ability to reveal previously known relationships and testable hypotheses in support of a deeper mechanistic understanding of ecological systems.
Availability and implementation: The data and code are available under the MIT License on GitHub at https://github.com/cghss-data-lab/uga-pipp.