Abstract
Many evidences show that microbes play vital roles in human health and diseases. Thus, predicting microbe-disease associations is helpful for disease prevention. In this study, we propose a predictive model called TNRGCN for microbe-disease associations based on Tripartite Network and Relation Graph Convolutional Network (RGCN). Firstly, we construct a microbe-disease-drug tripartite network through data processing from four databases. Secondly, we calculate similarity networks for microbes, diseases and drugs via microbe function similarity, disease semantic similarity and Gaussian interaction profile kernel similarity, respectively. Then, we utilize Principal Component Analysis (PCA) on similarity networks to extract main features of nodes in the tripartite network and input them as initial features to RGCN. Finally, according to the tripartite network and initial features, we apply two-layer RGCN to predict microbe-disease associations. Compared with other methods, TNRGCN achieves a good performance in cross validation. Meanwhile, case studies for diseases demonstrate TNRGCN has a good performance for predicting potential microbe-disease associations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Holmes, E., Wijeyesekera, A., Taylor-Robinson, S.D., et al.: The promise of metabolic phenotyping in gastroenterology and hepatology. Nat. Rev. Gastroenterol. Hepatol. 12(8), 458–471 (2015)
Sommer, F., Backhed, F.: The gut microbiota - masters of host development and physiology. Nat. Rev. Microbiol. 11(4), 227–238 (2013)
Gollwitzer, E.S., Saglani, S., Trompette, A., et al.: Lung microbiota promotes tolerance to allergens in neonates via PD-L1. Nat. Med. 20(6), 642–647 (2014)
Gill, S.R., Pop, M., DeBoy, R.T., et al.: Metagenomic analysis of the human distal gut microbiome. Science 312(5778), 1355–1359 (2006)
Shoaie, S., Ghaffari, P., Kovatcheva-Datchary, P., et al.: Quantifying diet-induced metabolic changes of the human gut microbiome. Cell Metab. 22(2), 320–331 (2015)
Cross, M.L.: Microbes versus microbes: immune signals generated by probiotic lactobacilli and their role in protection against microbial pathogens. FEMS Immunol. Med. Microbiol. 34(4), 245–253 (2002)
Dethlefsen, L., McFall-Ngai, M., Relman, D.A.: An ecological and evolutionary perspective on human-microbe mutualism and disease. Nature 449(7164), 811–818 (2007)
Yan, Q., Gu, Y., Li, X., et al.: Alterations of the Gut Microbiome in Hypertension. Front. Cell. Infect. Microbiol. 7, 381 (2017)
Rashid, T., Ebringer, A., Wilson, C.: The role of Klebsiella in Crohn’s disease with a potential for the use of antimicrobial measures. Int. J. Rheumatol. 2013, 610393 (2013)
Ma, W., Zhang, L., Zeng, P., et al.: An analysis of human microbe-disease associations. Brief. Bioinform. 18(1), 85–97 (2017)
Janssens, Y., Nielandt, J., Bronselaer, A., et al.: Disbiome database: linking the microbiome to disease. BMC Microbiol. 18(1), 50 (2018)
Chen, X., Huang, Y.A., You, Z.H., et al.: A novel approach based on KATZ measure to predict associations of human microbiota with non-infectious diseases. Bioinformatics 33(5), 733–739 (2017)
Li, H., Wang, Y.Q., Jiang, J.W., et al.: A Novel human microbe-disease association prediction method based on the bidirectional weighted network. Front. Microbiol. 10, 676 (2019)
Wu, C.Y., Gao, R., Zhang, D.L., et al.: PRWHMDA: human microbe-disease association prediction by random walk on the heterogeneous network with PSO. Int. J. Biol. Sci. 14(8), 849–857 (2018)
Qu, J., Zhao, Y., Yin, J.: Identification and analysis of human microbe-disease associations by matrix decomposition and label propagation. Front. Microbiol. 10, 291 (2019)
Long, Y., Luo, J., Zhang, Y., et al.: Predicting human microbe-disease associations via graph attention networks with inductive matrix completion. Brief. Bioinf. 22(3), bbaa146 (2021)
Tang, X., Luo, J., Shen, C., et al.: Multi-view multichannel attention graph convolutional network for miRNA-disease association prediction. Brief. Bioinf. (2021). https://doi.org/10.1093/bib/bbab174
Liu, L., Mamitsuka, H., Zhu, S.: HPOFiller: identifying missing protein-phenotype associations by graph convolutional network. Bioinformatics (2021). https://doi.org/10.1093/bioinformatics/btab224
Lei, X., Tie, J., Pan, Y.: Inferring metabolite-disease association using graph convolutional networks. IEEE/ACM Trans. Comput. Biol. Bioinf. (99), 1 (2021)
Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593–607. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_38
Lipscomb, C.E.: Medical subject headings (MeSH). Bull. Med. Libr. Assoc. 88(3), 265–266 (2000)
Wen, Z., Weitai, Y., Xiaoting, L., et al.: The bi-direction similarity integration method for predicting microbe-disease associations. IEEE Access 6, 38052–38061 (2018)
van Laarhoven, T., Nabuurs, S.B., Marchiori, E.: Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics 27(21), 3036–3043 (2011)
Yan, C., Duan, G.H., Wu, F.X., et al.: BRWMDA: Predicting microbe-disease associations based on similarities and bi-random walk on disease and microbe networks. IEEE-ACM Trans. Comput. Biol. Bioinf. 17(5), 1595–1604 (2020)
Zou, S., Zhang, J.P., Zhang, Z.P.: A novel approach for predicting microbe-disease associations by bi-random walk on the heterogeneous network. PLoS ONE 12(9), e0184394 (2017)
Luo, J.W., Long, Y.H.: NTSHMDA: prediction of human microbe-disease association based on random walk by integrating network topological similarity. IEEE-ACM Trans. Comput. Biol. Bioinf. 17(4), 1341–1351 (2020)
Li, S., Xie, M., Liu, X.: A novel approach based on bipartite network recommendation and KATZ model to predict potential micro-disease associations. Front. Genet. 10, 1147 (2019)
Bao, W., Jiang, Z., Huang, D.S.: Novel human microbe-disease association prediction using network consistency projection. BMC Bioinf. 18(S16), 543 (2017)
Huang, Z.A., Chen, X., Zhu, Z.X., et al.: PBHMDA: path-based human microbe-disease association prediction. Front. Microbiol. 8, 2560 (2017)
Yin, M.-M., Liu, J.-X., Gao, Y.-L., et al.: NCPLP: a novel approach for predicting microbe-associated diseases with network consistency projection and label propagation. IEEE Trans. Cybern. (99), 1–9 (2020)
Sung, H., Ferlay, J., Siegel, R.L., et al.: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71(3), 209–249 (2021)
Ekanayake, A., Madegedara, D., Chandrasekharan, V., Magana-Arachchi, D.: Respiratory bacterial microbiota and individual bacterial variability in lung cancer and bronchiectasis patients. Indian J. Microbiol. 60(2), 196–205 (2019). https://doi.org/10.1007/s12088-019-00850-w
Zhang, M., Zhou, H., Xu, S.S., et al.: The gut microbiome can be used to predict the gastrointestinal response and efficacy of lung cancer patients undergoing chemotherapy. Ann. Palliative Med. 9(6), 4211–4227 (2020)
Cheng, C., Wang, Z., Wang, J., et al.: Characterization of the lung microbiome and exploration of potential bacterial biomarkers for lung cancer. Transl. Lung Cancer Res. 9(3), 693–704 (2020)
Lee, S.H., Sung, J.Y., Yong, D., et al.: Characterization of microbiome in bronchoalveolar lavage fluid of patients with lung cancer comparing with benign mass like lesions. Lung Cancer 102, 89–95 (2016)
Bello, S., Vengoechea, J.J., Ponce-Alonso, M., et al.: Core microbiota in central lung cancer with streptococcal enrichment as a possible diagnostic marker. Arch. Bronconeumol. (Engl. Ed) (2020). https://doi.org/10.1016/j.arbres.2020.05.034
Zheng, L., Xu, J., Sai, B., et al.: Microbiome related cytotoxically active CD8+ TIL are inversely associated with lung cancer development. Front Oncol 10, 531131 (2020)
Cole, T.J., Bellizzi, M.C., Flegal, K.M., et al.: Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ 320(7244), 1240–1243 (2000)
Petersen, C., Bell, R., Kiag, K.A., et al.: T cell-mediated regulation of the microbiota protects against obesity. Science 365(6451), 340 (2019)
Gao, R.Y., Zhu, C.L., Li, H., et al.: Dysbiosis signatures of gut microbiota along the sequence from healthy, young patients to those with overweight and obesity. Obesity 26(2), 351–361 (2018)
Andoh, A., Nishida, A., Takahashi, K., et al.: Comparison of the gut microbial community between obese and lean peoples using 16S gene sequencing in a Japanese population. J. Clin. Biochem. Nutr. 59(1), 65–70 (2016)
Jie, Z., Yu, X., Liu, Y., et al.: The baseline gut microbiota directs dieting-induced weight loss trajectories. Gastroenterology 160(6), 2029-2042.e2016 (2021)
Zeigler, C.C., Persson, G.R., Wondimu, B., et al.: Microbiota in the oral subgingival biofilm is associated with obesity in adolescence. Obesity (Silver Spring) 20(1), 157–164 (2012)
Moreno-Indias, I., Sanchez-Alcoholado, L., Perez-Martinez, P., et al.: Red wine polyphenols modulate fecal microbiota and reduce markers of the metabolic syndrome in obese patients. Food Funct. 7(4), 1775–1787 (2016)
Acknowledgment
We thank the financial support from National Natural Science Foundation of China under Grant Nos. 61972451, 61902230, and the Fundamental Research Funds for the Central Universities of China under Grant No. GK201901010.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, Y., Lei, X., Pan, Y. (2021). Predicting Microbe-Disease Association via Tripartite Network and Relation Graph Convolutional Network. In: Wei, Y., Li, M., Skums, P., Cai, Z. (eds) Bioinformatics Research and Applications. ISBRA 2021. Lecture Notes in Computer Science(), vol 13064. Springer, Cham. https://doi.org/10.1007/978-3-030-91415-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-91415-8_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-91414-1
Online ISBN: 978-3-030-91415-8
eBook Packages: Computer ScienceComputer Science (R0)