Larmande Pierre, Pitollat Bertrand, Tando Ndomassi, Pomie Yann, Happi Bill, Guignon Valentin, Ruiz Manuel.
2023. AgroLD: a knowledge graph for the plant sciences.
In : 14th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences (SWAT4HCLS 2023). Yamaguchi Atsuko (ed.), Splendiani Andrea (ed.), Scott Marshall M. (ed.) et al.
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Url - éditeur : https://ceur-ws.org/Vol-3415/
Résumé : Recent advances in high-throughput technologies have revolutionized the analysis in the field of the plant sciences. However, there is an urgent need to effectively integrate and assimilate complementary information to understand the biological system in its entirety. We have developed AgroLD, a knowledge graph that exploits Semantic Web technologies to integrate data of interest for the plant science community e.g., rice, wheat, arabidopsis and in this way facilitate the formulation and validation of new scientific hypotheses. AgroLD contains around 900M triples created by annotating and integrating more than 100 datasets coming from 15 data sources. Our objective is to offer a domain specific knowledge platform to answer complex biological and plant sciences questions related to the implication of genes in, for instance, plant disease resistance or adaptative responses to climate change. In this demo, we present some results which currently focused on genomics, genetics and trait associations.
Auteurs et affiliations
- Larmande Pierre, IRD (FRA)
- Pitollat Bertrand, CIRAD-BIOS-UMR AGAP (FRA)
- Tando Ndomassi, IRD (FRA)
- Pomie Yann, IRD (FRA)
- Happi Bill, IRD (FRA)
- Guignon Valentin, CIRAD-BIOS-UMR AGAP (FRA)
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Ruiz Manuel, CIRAD-BIOS-UMR AGAP (FRA)
ORCID: 0000-0001-8153-276X
Source : Cirad-Agritrop (https://agritrop.cirad.fr/611171/)
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