Gantsooj Demberel Temuulen Dorjsuren Yansen Su Dolgorsuren Batjargal


Through this research work, we aimed to use a graph model to determine important drug substances that are proper for the Echinococcosis hydatid disease. Also, that model can be used in convergence with other similar diseases due to the genes of the Echinococcus granulosus that causes this parasitic disease, and to analyze it by applying graph algorithms to the established model. Furthermore, there is an urgent need to create a basic model for machine learning and artificial intelligence methods in health and pharmaceuticals. Also, the collection of genes and proteins registered in the official internationally recognized gene-disease databases, as well as the data of drugs and pharmaceutical products used for the specific disease, is the first step in the understanding and development of interdisciplinary science. In this paper, we define a heterogeneous graph with multiple types of nodes and edges as a basic model for future studies, as well as the methods and algorithms used in the study, considering the interrelationship between Echinococcus granulosus genes, proteins, diseases, and drugs.

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echinococcus, drug interaction network, graph theory, data analysis

[1] National Centre for Zoonotic Disease; Last Accessed: March. 29, 2022. [Online]. Available: https://nczd.gov.mn/?p=9882
[2] Yu, Y., Li, J., Wang, W., Wang, T., Qi, W., Zheng, X., ... & Duan, L. (2021). Transcriptome analysis uncovers the key pathways and candidate genes related to the treatment of Echinococcus granulosus protoscoleces with the repurposed drug pyronaridine. BMC genomics, 22(1), 1-9.
[3] A study of Echinococcosis in animal and livestock; Last Accessed: March. 29, 2022. [Online]. Available: https://old.legalinfo.mn/annex/details/9124?lawid=13970
[4] P. Nyamdavaa., (2010) Directory of Infectious Disease Control, American Public Health Association, World Health Organization, (United Book Press Inc.)
[5] National Center for Biotechnology Information (NCBI)[Internet]. Bethesda (MD): National Library of Medicine (US), National Center for Biotechnology Information; [1988] – [cited 2022 Sep 28]. Available from: https://www.ncbi.nlm.nih.gov/
[6] Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J. Drugbank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2006 Jan 1;34 (Database issue): D668-72. 16381955. Last Accessed: March. 29, 2022. [Online]. Available: https://go.drugbank.com/
[7] Breuer et al., InnateDB: systems biology of innate immunity and beyond - recent updates and continuing curation. Nucl. Acids Res. (2013) 41 (D1); Last Accessed: March. 29, 2022. [Online]. Available: https://www.innatedb.com/index.jsp
[8] Janet Piñero, Juan Manuel Ramírez-Anguita, Josep Saüch-Pitarch, Francesco Ronzano, Emilio Centeno, Ferran Sanz, Laura I Furlong. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucl. Acids Res. (2019) doi:10.1093/nar/gkz1021; Last Accessed: March. 29, 2022. [Online]. Available: https://www.disgenet.org/
[9] Zhou, G., Soufan, O., Ewald, J., Hancock, REW, Basu, N. and Xia, J. (2019) "NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis" Nucleic Acids Research 47 (W1): W234-W241.
[10] Cheng, F., & Zhao, Z. (2014). Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. Journal of the American Medical Informatics Association, 21(e2), e278-e286.
[11] Zhou, F., Malher, S., & Toivonen, H. (2010, December). Network simplification with minimal loss of connectivity. In 2010 IEEE international conference on data mining (pp. 659-668). IEEE.
[12] Hagberg, A., Swart, P., & S Chult, D. (2008). Exploring network structure, dynamics, and function using NetworkX (No. LA-UR-08-05495; LA-UR-08-5495). Los Alamos National Lab.(LANL), Los Alamos, NM (United States)
[13] Kerrache, S. (2021). LinkPred: a high performance library for link prediction in complex networks. PeerJ Computer Science, 7, e521.
[14] Nushi, E., Popescu, V.-B., Angel Sanchez Martin, J., Ivanov, S., & Czeizler, E. (2021). Network modeling methods for precision medicine.
[15] Muzio, G., O’Bray, L., & Borgwardt, K. (2021). Biological network analysis with deep learning. Briefings in bioinformatics, 22(2), 1515-1530.
[16] Jamasb, A. R., Day, B., Cangea, C., Liò, P., & Blundell, T. L. (2021). Deep learning for protein–protein interaction site prediction. In Proteomics Data Analysis (pp. 263-288). Humana, New York, NY.
[17] Yuan, Q., Chen, J., Zhao, H., Zhou, Y., & Yang, Y. (2022). Structure-aware protein–protein interaction site prediction using deep graph convolutional network. Bioinformatics, 38(1), 125-132.
[18] Gaudelet, T., Day, B., Jamasb, A. R., Soman, J., Regep, C., Liu, G., ... & Taylor-King, J. P. (2021). Utilizing graph machine learning within drug discovery and development. Briefings in bioinformatics, 22(6), bbab159.
[19] Raimondi, D., Simm, J., Arany, A., & Moreau, Y. (2021). A novel method for data fusion over entityrelation graphs and its application to protein–protein interaction prediction. Bioinformatics, 37(16), 2275-2281.
[20] Coşkun, M., & Koyutürk, M. (2021). Node similarity-based graph convolution for link prediction in biological networks. Bioinformatics, 37(23), 4501-4508.
[21] Yang, J., Xu, Z., Wu, W. K. K., Chu, Q., & Zhang, Q. (2021). GraphSynergy: a network-inspired deep learning model for anticancer drug combination prediction. Journal of the American Medical Informatics Association, 28(11), 2336-2345.
[22] Hu, L., Zhang, J., Pan, X., Yan, H., & You, Z. H. (2021). HiSCF: leveraging higher-order structures for clustering analysis in biological networks. Bioinformatics, 37(4), 542-550.
[23] Rekik, I., & Gurbuz, M. B. (2021). MGN-Net: a multi-view graph normalizer for integrating heterogeneous biological network populations. arXiv preprint arXiv:2104.03895.
[24] Awan, Z., Alrayes, N., Khan, Z., Almansouri, M., Bima, A. I. H., Almukadi, H., ... & Banaganapalli, B. (2022). Identifying significant genes and functionally enriched pathways in familial hypercholesterolemia using integrated gene co-expression network analysis. Saudi Journal of Biological Sciences, 29(5), 3287-3299.
[25] List of priority diseases for zoonotic disease surveillance and research, (2012) Last Accessed: September. 26, 2022. https://moh.gov.mn/uploads/files/7e67621d23cdc96a989ec8f3400e24bc.pdf
[26] Gebreyes WA, Dupouy-Camet J, Newport MJ, Oliveira CJ, Schlesinger LS, Saif YM, et al. The global One Health paradigm: challenges and opportunities for tackling infectious diseases at the human, animal, and environment interface in low-resource settings. PLoS Negl Trop Dis. 2014;8:e3257. 10.1371/journal.pntd.0003257
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How to Cite
Demberel, G., Dorjsuren, T., Su, Y., & Batjargal, D. (2022). A Heterogeneous Graph Model for Repurposing Drugs against Echinococcosis. ICT Focus, 1(1), 35–46. https://doi.org/10.58873/sict.v1i1.30