Leveraging Biotic Interaction Knowledge Graph and Network Analysis to Uncover Insect Vectors of Plant Virus
Downloads
Background: Insect vectors spread 80% of plant viruses, causing major agricultural production losses. Direct insect vector identification is difficult due to a wide range of hosts, limited detection methods, and high PCR costs and expertise. Currently, a biodiversity database named Global Biotic Interaction (GloBI) provides an opportunity to identify virus vectors using its data.
Objective: This study aims to build an insect vector search engine that can construct an virus-insect-plant interaction knowledge graph, identify insect vectors using network analysis, and extend knowledge about identified insect vectors.
Methods: We leverage GloBI data to construct a graph that shows the complex relationships between insects, viruses, and plants. We identify insect vectors using interaction analysis and taxonomy analysis, then combine them into a final score. In interaction analysis, we propose Targeted Node Centric-Degree Centrality (TNC-DC) which finds insects with many directly and indirectly connections to the virus. Finally, we integrate Wikidata, DBPedia, and NCBIOntology to provide comprehensive information about insect vectors in the knowledge extension stage.
Results: The interaction graph for each test virus was created. At the test stage, interaction and taxonomic analysis achieved 0.80 precision. TNC-DC succeeded in overcoming the failure of the original degree centrality which always got bees in the prediction results. During knowledge extension stage, we succeeded in finding the natural enemy of the Bemisia Tabaci (an insect vector of Pepper Yellow Leaf Curl Virus). Furthermore, an insect vector search engine is developed. The search engine provides network analysis insights, insect vector common names, photos, descriptions, natural enemies, other species, and relevant publications about the predicted insect vector.
Conclusion: An insect vector search engine correctly identified virus vectors using GloBI data, TNC-DC, and entity embedding. Average precision was 0.80 in precision tests. There is a note that some insects are best in the first-to-five order.
Keywords: Knowledge Graph, Network Analysis, Degree Centrality, Entity Embedding, Insect Vector
A. E. Whitfield, B. W. Falk, and D. Rotenberg, "Insect vector-mediated transmission of plant viruses,” in Virology, vol. 479–480, pp. 278–289, 2015, doi: https://doi.org/10.1016/j.virol.2015.03.026.
M. Deshoux, B. Monsion, and M. Uzest, "Insect cuticular proteins and their role in transmission of phytoviruses,” in Curr. Opin. Virol., vol. 33, pp. 137–143, 2018, doi: https://doi.org/10.1016/j.coviro.2018.07.015.
M. A. Catto, H. Mugerwa, B. K. Myers, S. Pandey, B. Dutta, and R. Srinivasan, "A review on transcriptional responses of interactions between insect vectors and plant viruses,” in Cells, vol. 11, no. 4, p. 693, 2022.
Y. S. Pauzi, "Deteksi Garlic common latent virus dan Shallot latent virus pada Beberapa Fase Pertumbuhan Tanaman Bawang Merah dan Bawang Putih [skripsi],” Bogor: Institut Pertanian Bogor, 2017.
Meliyana, "Virus Infection and Growth Performance of Several Local Garlic Cultivars [skripsi],” Bogor: Institut Pertanian Bogor, 2020.
Y. M. A. Sandra, "Penapisan Dan Identifikasi Karakter Ketahanan Terhadap Virus Gemini Dan Kutu Kebul Pada Cabai [tesis],” Bogor: Institut Pertanian Bogor, 2022.
D. Wahyudin, "Penapisan Ketahanan Galur dan Taksasi Kehilangan Hasil Tomat (Lycopersicum esculentum L.) Terhadap infeksi Tomato chlorosis crinivirus [tesis],” Bogor: Institut Pertanian Bogor, 2022.
C. Lilies, "Kunci determinasi serangga,” in Kanisius. Yogyakarta, vol. 223, 1991.
M. Heviyanti and M. Syahril, "Identifikasi serangga hama tanaman padi sawah (Oryza sativa L.) di Desa Paya Rahat, Kecamatan Banda Mulia, Kabupaten Aceh Tamiang,” in Prosiding Seminar Nasional Pertanian, 2018.
H. Lee et al., "Insect vector manipulation by a plant virus and simulation modeling of its potential impact on crop infection,” in Sci. Rep., vol. 12, no. 1, p. 8429, 2022.
L. Rubio, L. Galipienso, and I. Ferriol, "Detection of plant viruses and disease management: Relevance of genetic diversity and evolution,” in Front. Plant Sci., vol. 11, p. 1092, 2020.
S. dan N. Yasin, "Karakterisasi Kutu Kebul (Bemisia Tabaci) Sebagai Vektor Virus Gemini Dengan Teknik Pcr-rapd,” in J. Hama dan Penyakit Tumbuh. Trop., vol. 6, no. 2, pp. 113–119, 2006, doi: 10.23960/j.hptt.26113-119.
L. Xiaoxue, B. Xuesong, W. Longhe, R. Bingyuan, L. Shuhan, and L. Lin, "Review and trend analysis of knowledge graphs for crop pest and diseases,” in IEEE Access, vol. 7, pp. 62251–62264, 2019.
D. Wood, M. Zaidman, L. Ruth, and M. Hausenblas, Linked Data : Structured data on the web. New York, NY, USA: Manning Publications Co., 2014.
A. L. Barabasi, M. Tillich, K. Albrecht, M. Martino, M. Pósfa, and G. Musella, Network Science Book. Cambridge: Cambridge University Press, 2015. [Online]. Available: http://barabasi.com/networksciencebook/
J. H. Poelen, J. D. Simons, and C. J. Mungall, "Global biotic interactions: An open infrastructure to share and analyze species-interaction datasets,” in Ecol. Inform., vol. 24, pp. 148–159, 2014.
M. M. Yusof, N. F. Rosli, M. Othman, R. Mohamed, and M. H. A. Abdullah, "M-DCocoa: M-agriculture expert system for diagnosing cocoa plant diseases,” in Recent Advances on Soft Computing and Data Mining: Proceedings of the Third International Conference on Soft Computing and Data Mining (SCDM 2018), Johor, Malaysia, February 06-07, 2018, Springer, 2018, pp. 363–371.
W. Jearanaiwongkul, C. Anutariya, and F. Andres, "An ontology-based approach to plant disease identification system,” in Proceedings of the 10th International Conference on Advances in Information Technology, 2018, pp. 1–8.
K. Lagos-Ortiz, M. D. Salas-Zárate, M. A. Paredes-Valverde, J. A. García-Díaz, and R. Valencia-García, "AgriEnt: A Knowledge-Based Web Platform for Managing Insect Pests of Field Crops,” Applied Sciences, vol. 10, no. 3. 2020. doi: 10.3390/app10031040.
W. Jearanaiwongkul, C. Anutariya, T. Racharak, and F. Andres, "An Ontology-Based Expert System for Rice Disease Identification and Control Recommendation,” in Appl. Sci., vol. 11, no. 21, p. 10450, 2021.
M. Á. Rodriguez-Garcia, F. García-Sánchez, and R. Valencia-García, "Knowledge-Based System for Crop Pests and Diseases Recognition,” Electronics, vol. 10, no. 8. 2021. doi: 10.3390/electronics10080905.
K. A. Garrett et al., "Network analysis: a systems framework to address grand challenges in plant pathology,” in Annu. Rev. Phytopathol., vol. 56, pp. 559–580, 2018.
A. Bundy and L. Wallen, "Breadth-first search,” in Cat. Artif. Intell. tools, p. 13, 1984.
L. C. Freeman, "Centrality in social networks conceptual clarification,” in Soc. Networks, vol. 1, no. 3, pp. 215–239, 1978.
B. Steenwinckel, G. Vandewiele, T. Agozzino, and F. Ongenae, "pyRDF2Vec: A Python Implementation and Extension of RDF2Vec,” in European Semantic Web Conference, Springer, 2023, pp. 471–483.
P.-E. Danielsson, "Euclidean distance mapping,” in Comput. Graph. image Process., vol. 14, no. 3, pp. 227–248, 1980.
J. Lehmann et al., "Dbpedia–a large-scale, multilingual knowledge base extracted from wikipedia,” in Semant. Web, vol. 6, no. 2, pp. 167–195, 2015.
M. Farda-Sarbas and C. Mueller-Birn, "Wikidata from a Research Perspective--A Systematic Mapping Study of Wikidata,” in arXiv Prepr. arXiv1908.11153, 2019.
S. Schulz, H. Stenzhorn, and M. Boeker, "The ontology of biological taxa,” in Bioinformatics, vol. 24, no. 13, pp. i313–i321, 2008.
E. Arnaud et al., "The ontologies community of practice: A CGIAR initiative for big data in agrifood systems,” in Patterns, vol. 1, no. 7, p. 100105, 2020.
A. Hogan et al., "Knowledge graphs,” in ACM Comput. Surv., vol. 54, no. 4, pp. 1–37, 2021.
K. Seltmann, J. Poelen, K. Sullivan, and J. Zaspel, "Making Parasite-Host Associations Visible using Global Biotic Interactions,” in Biodivers. Inf. Sci. Stand., vol. 4, 2020.
Y. Do and M. B. Choi, "Network analysis for co"occurrence of pest insects on host crops,” in Entomol. Res., vol. 49, no. 1, pp. 35–45, 2019.
E. Delmas et al., "Analysing ecological networks of species interactions,” in Biol. Rev., vol. 94, no. 1, pp. 16–36, 2019.
T. Strydom et al., "A roadmap towards predicting species interaction networks (across space and time),” in Philos. Trans. R. Soc. B, vol. 376, no. 1837, p. 20210063, 2021.
A. Hagberg, P. Swart, and D. S Chult, "Exploring network structure, dynamics, and function using NetworkX,” Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2008.
P. Ristoski and H. Paulheim, "Rdf2vec: Rdf graph embeddings for data mining,” in The Semantic Web–ISWC 2016: 15th International Semantic Web Conference, Kobe, Japan, October 17–21, 2016, Proceedings, Part I 15, Springer, 2016, pp. 498–514.
G. Vandewiele, B. Steenwinckel, T. Agozzino, and F. Ongenae, "pyRDF2Vec: A Python Implementation and Extension of RDF2Vec,” in arXiv Prepr. arXiv2205.02283, 2022.
G. M. Angelella and T. D. Waters, "Afidopyropen as a potential tool for Potato leafroll virus management in post-neonicotinoid potato production,” in J. Econ. Entomol., vol. 116, no. 3, pp. 713–718, 2023, doi: https://doi.org/10.1093/jee/toad042.
R. Thangjam, V. Kadam, P. D. Nath, and R. K. Borah, "Insect Vectors Associated with Viral Diseases of King Chilli (Capsicum Chinense Jacq.) in North East India,” in Indian J. Entomol., pp. 1–4, 2022.
M. F. M. Santiago, K. C. King, and G. C. Drew, "Interactions between insect vectors and plant pathogens span the parasitism–mutualism continuum,” in Biol. Lett., vol. 19, no. 3, p. 20220453, 2023.
C. Goutte and E. Gaussier, "A probabilistic interpretation of precision, recall and F-score, with implication for evaluation,” in Advances in Information Retrieval: 27th European Conference on IR Research, ECIR 2005, Santiago de Compostela, Spain, March 21-23, 2005. Proceedings 27, Springer, 2005, pp. 345–359.
F. Darari, "COVIWD: COVID-19 Wikidata Dashboard,” in J. Ilmu Komput. dan Inf., vol. 14, no. 1, pp. 39–47, 2021.
P. Mittapelly and S. P. Rajarapu, "Applications of Proteomic Tools to Study Insect Vector–Plant Virus Interactions,” Life, vol. 10, no. 8. 2020. doi: 10.3390/life10080143.
Copyright (c) 2024 The Authors. Published by Universitas Airlangga.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
All accepted papers will be published under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. Authors retain copyright and grant the journal right of first publication. CC-BY Licenced means lets others to Share (copy and redistribute the material in any medium or format) and Adapt (remix, transform, and build upon the material for any purpose, even commercially).
