Skip to main content
SpringerLink
Log in

Relation Predictions in Comorbid Disease Centric Knowledge Graph Using Heterogeneous GNN Models

  • Conference paper
  • First Online:
Bioinformatics and Biomedical Engineering (IWBBIO 2023)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13920))

  • 750 Accesses

Abstract

Disease comorbidity has been an important topic of research for the last decade. This topic has become more popular due to the recent outbreak of COVID-19 disease. A comorbid condition due to multiple concurrent diseases is more fatal than a single disease. These comorbid conditions can be caused due to different genetic as well as drug-related side effects on an individual. There are already successful methods for predicting comorbid disease associations. This disease-associated genetic or drug-invasive information can help infer more target factors that cause common diseases. This may further help find out effective drugs for treating a pair of concurrent diseases. In addition to that, the common drug side-effects causing a disease phenotype and the gene associated with that can be helpful in finding important biomarkers for further prognosis of the comorbid disease. In this paper, we use the knowledge graph (KG) from our previous study to find out target-specific relations apart from sole disease-disease associations. We use four different heterogeneous graph neural network models to perform link prediction among different entities in the knowledge graph and we perform a comparative analysis among them. It is found that our best heterogeneous GNN model outperforms existing state-of-the-art models on a few target-specific relationships. Further, we also predict a few novel drug-disease, drug-phenotype, disease-phenotype, and gene-phenotype associations. These interrelated associations are further used to find out the common phenotypes associated with a comorbid disease as well as caused by the direct side effects of a treating drug. In this regard, our methodology also predicts some novel biomarkers and therapeutics for different fatal prevalent diseases.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abbas, K., et al.: Application of network link prediction in drug discovery. BMC Bioinf. 22, 1–21 (2021)

    Article  Google Scholar 

  2. Acar, E., Dunlavy, D.M., Kolda, T.G.: Link prediction on evolving data using matrix and tensor factorizations. In: 2009 IEEE International Conference on Data Mining Workshops, pp. 262–269. IEEE (2009)

    Google Scholar 

  3. Alshahrani, M., Hoehndorf, R.: Drug repurposing through joint learning on knowledge graphs and literature. Biorxiv, p. 385617 (2018)

    Google Scholar 

  4. Alshahrani, M., Khan, M.A., Maddouri, O., Kinjo, A.R., Queralt-Rosinach, N., Hoehndorf, R.: Neuro-symbolic representation learning on biological knowledge graphs. Bioinformatics 33(17), 2723–2730 (2017)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Ashburner, M., et al.: Gene ontology: tool for the unification of biology. Nat. Genet. 25(1), 25–29 (2000)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Ayuso Muñoz, A., et al.: Redirection: generating drug repurposing hypotheses using link prediction with DISNET data. bioRxiv, pp. 2022–07 (2022)

    Google Scholar 

  7. Baradaran, A., Ebrahimzadeh, M.H., Baradaran, A., Kachooei, A.R.: Prevalence of comorbidities in COVID-19 patients: a systematic review and meta-analysis. Arch. Bone Joint Surg. 8(Suppl 1), 247 (2020)

    PubMed  PubMed Central  Google Scholar 

  8. Barbagallo, M., Dominguez, L.J.: Type 2 diabetes mellitus and Alzheimer’s disease. World J. Diabetes 5(6), 889 (2014)

    Article  PubMed  PubMed Central  Google Scholar 

  9. Becker, K.G., Barnes, K.C., Bright, T.J., Wang, S.A.: The genetic association database. Nat. Genet. 36(5), 431–432 (2004)

    Article  CAS  PubMed  Google Scholar 

  10. Biswas, S., Mitra, P., Rao, K.S.: Relation prediction of co-morbid diseases using knowledge graph completion. IEEE/ACM Trans. Comput. Biol. Bioinf. 18(2), 708–717 (2019)

    Article  Google Scholar 

  11. Breit, A., Ott, S., Agibetov, A., Samwald, M.: Openbiolink: a benchmarking framework for large-scale biomedical link prediction. Bioinformatics 36(13), 4097–4098 (2020)

    Article  CAS  PubMed  Google Scholar 

  12. Cai, L., Li, J., Wang, J., Ji, S.: Line graph neural networks for link prediction. IEEE Trans. Pattern Anal. Mach. Intell. (2021)

    Google Scholar 

  13. Chen, J., et al.: E-LSTM-D: A deep learning framework for dynamic network link prediction. IEEE Trans. Syst. Man Cybern.: Syst. 51(6), 3699–3712 (2019)

    Article  Google Scholar 

  14. Cheong, J.L., de Pablo-Fernandez, E., Foltynie, T., Noyce, A.J.: The association between type 2 diabetes mellitus and Parkinson’s disease. J. Parkinson’s Dis. 10(3), 775–789 (2020)

    Article  CAS  Google Scholar 

  15. Chiu, C., Zhan, J.: Deep learning for link prediction in dynamic networks using weak estimators. IEEE Access 6, 35937–35945 (2018)

    Article  Google Scholar 

  16. Consortium, U.: Uniprot: a hub for protein information. Nucleic Acids Res. 43(D1), D204–D212 (2015)

    Google Scholar 

  17. Coşkun, M., Koyutürk, M.: Node similarity-based graph convolution for link prediction in biological networks. Bioinformatics 37(23), 4501–4508 (2021)

    Article  PubMed  PubMed Central  Google Scholar 

  18. Crichton, G., Guo, Y., Pyysalo, S., Korhonen, A.: Neural networks for link prediction in realistic biomedical graphs: a multi-dimensional evaluation of graph embedding-based approaches. BMC Bioinf. 19(1), 1–11 (2018)

    Article  Google Scholar 

  19. Curado, M.: Return random walks for link prediction. Inf. Sci. 510, 99–107 (2020)

    Article  Google Scholar 

  20. Davis, A.P., et al.: A CTD-Pfizer collaboration: manual curation of 88 000 scientific articles text mined for drug-disease and drug-phenotype interactions. Database 2013 (2013)

    Google Scholar 

  21. De Raedt, L., Kimmig, A., Toivonen, H.: Problog: A probabilistic prolog and its application in link discovery. In: IJCAI, vol. 7, pp. 2462–2467. Hyderabad (2007)

    Google Scholar 

  22. Domingos, P., Kok, S., Lowd, D., Poon, H., Richardson, M., Singla, P.: Markov logic. In: De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S. (eds.) Probabilistic Inductive Logic Programming. LNCS (LNAI), vol. 4911, pp. 92–117. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78652-8_4

    Chapter  Google Scholar 

  23. Dunlavy, D.M., Kolda, T.G., Acar, E.: Temporal link prediction using matrix and tensor factorizations. ACM Trans. Knowl. Disc. Data (TKDD) 5(2), 1–27 (2011)

    Article  Google Scholar 

  24. Ejaz, H., et al.: Covid-19 and comorbidities: deleterious impact on infected patients. J. Infect. Pub. Health 13(12), 1833–1839 (2020)

    Article  Google Scholar 

  25. Fabregat, A., et al.: Reactome graph database: efficient access to complex pathway data. PLoS Comput. Biol. 14(1), e1005968 (2018)

    Article  PubMed  PubMed Central  Google Scholar 

  26. Hamosh, A., Scott, A.F., Amberger, J., Valle, D., McKusick, V.A.: Online mendelian inheritance in man (OMIM). Hum. Mutat. 15(1), 57–61 (2000)

    Article  CAS  PubMed  Google Scholar 

  27. Han, H., et al.: Openhgnn: an open source toolkit for heterogeneous graph neural network. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pp. 3993–3997 (2022)

    Google Scholar 

  28. Hu, Z., Dong, Y., Wang, K., Sun, Y.: Heterogeneous graph transformer. In: Proceedings of The Web Conference 2020, pp. 2704–2710 (2020)

    Google Scholar 

  29. Huang, J.Y., et al.: The risk of endometrial cancer and uterine sarcoma following endometriosis or pelvic inflammatory disease. Cancers 15(3), 833 (2023)

    Article  PubMed  PubMed Central  Google Scholar 

  30. Ji, H., Wang, X., Shi, C., Wang, B., Yu, P.: Heterogeneous graph propagation network. IEEE Trans. Knowl. Data Eng. (2021)

    Google Scholar 

  31. Kherraf, Z.E., et al.: Spink 2 deficiency causes infertility by inducing sperm defects in heterozygotes and azoospermia in homozygotes. EMBO Mol. Med. 9(8), 1132–1149 (2017)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kibbe, W.A., et al.: Disease ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data. Nucleic Acids Res. 43(D1), D1071–D1078 (2015)

    Article  CAS  PubMed  Google Scholar 

  33. Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)

  34. Köhler, S., et al.: The human phenotype ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res. 42(D1), D966–D974 (2014)

    Article  PubMed  Google Scholar 

  35. Kuhn, M., Campillos, M., Letunic, I., Jensen, L.J., Bork, P.: A side effect resource to capture phenotypic effects of drugs. Mol. Syst. Biol. 6(1), 343 (2010)

    Article  PubMed  PubMed Central  Google Scholar 

  36. Lee, J., et al.: Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4), 1234–1240 (2020)

    Article  CAS  PubMed  Google Scholar 

  37. Li, X., Song, D., Leng, S.X.: Link between type 2 diabetes and Alzheimer’s disease: from epidemiology to mechanism and treatment. Clin. Intervent. Aging, 549–560 (2015)

    Google Scholar 

  38. Liu, F., Liu, B., Sun, C., Liu, M., Wang, X.: Deep learning approaches for link prediction in social network services. In: Lee, M., Hirose, A., Hou, Z.-G., Kil, R.M. (eds.) ICONIP 2013. LNCS, vol. 8227, pp. 425–432. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-42042-9_53

    Chapter  Google Scholar 

  39. Liu, T., Pan, X., Wang, X., Feenstra, K.A., Heringa, J., Huang, Z.: Exploring the microbiota-gut-brain axis for mental disorders with knowledge graphs. J. Artif. Intell. Med. Sci. 1(3–4), 30–42 (2021)

    Google Scholar 

  40. Liu, T., Pan, X., Wang, X., Feenstra, K.A., Heringa, J., Huang, Z.: Predicting the relationships between gut microbiota and mental disorders with knowledge graphs. Health Inf. Sci. Syst. 9, 1–9 (2021)

    Article  Google Scholar 

  41. Liu, W., Yin, L., Wang, C., Liu, F., Ni, Z., et al.: Multitask healthcare management recommendation system leveraging knowledge graph. J. Healthc. Eng. 2021 (2021)

    Google Scholar 

  42. Long, Y.: Pre-training graph neural networks for link prediction in biomedical networks. Bioinformatics 38(8), 2254–2262 (2022)

    Article  CAS  PubMed  Google Scholar 

  43. Marantelli, S., Hand, R., Carapetis, J., Beaton, A., Wyber, R.: Severe adverse events following benzathine penicillin g injection for rheumatic heart disease prophylaxis: cardiac compromise more likely than anaphylaxis. Heart Asia 11(2) (2019)

    Google Scholar 

  44. Mutlu, M.F., et al.: Two cases of first onset intrahepatic cholestasis of pregnancy associated with moderate ovarian hyperstimulation syndrome after IVF treatment and review of the literature. J. Obstet. Gynaecol. 37(5), 547–549 (2017)

    Article  PubMed  Google Scholar 

  45. Patel, R., Guo, Y., Alhudhaif, A., Alenezi, F., Althubiti, S.A., Polat, K.: Graph-based link prediction between human phenotypes and genes. Math. Prob. Eng. 2022 (2021)

    Google Scholar 

  46. Pham, C., Dang, T.: Link prediction for biomedical network. In: The 12th International Conference on Advances in Information Technology, pp. 1–5 (2021)

    Google Scholar 

  47. Sanyaolu, A., et al.: Comorbidity and its impact on patients with Covid-19. SN Compr. Clin. Med. 2, 1069–1076 (2020)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. 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

    Chapter  Google Scholar 

  49. Szklarczyk, D., et al.: The string database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 39(suppl_1), D561–D568 (2010)

    Google Scholar 

  50. Wang, M.Y.: Deep graph library: towards efficient and scalable deep learning on graphs. In: ICLR Workshop on Representation Learning on Graphs and Manifolds (2019)

    Google Scholar 

  51. Wang, X., et al.: Heterogeneous graph attention network. In: The World Wide Web Conference, pp. 2022–2032 (2019)

    Google Scholar 

  52. Yun, S., Kim, S., Lee, J., Kang, J., Kim, H.J.: Neo-GNNs: neighborhood overlap-aware graph neural networks for link prediction. Adv. Neural. Inf. Process. Syst. 34, 13683–13694 (2021)

    Google Scholar 

  53. Zhang, C., Zhang, H., Yuan, D., Zhang, M.: Deep learning based link prediction with social pattern and external attribute knowledge in bibliographic networks. In: 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 815–821. IEEE (2016)

    Google Scholar 

  54. Zhang, M., Chen, Y.: Link prediction based on graph neural networks. In: Advances in Neural Information Processing Systems, vol. 31 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Advanced Technology Development Centre, Indian Institute of Technology, Kharagpur, India

    Saikat Biswas

  2. Mathematics and Computing, Indian Institute of Technology, Kharagpur, India

    Koushiki Dasgupta Chaudhuri

  3. Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, India

    Pabitra Mitra & Krothapalli Sreenivasa Rao

Authors
  1. Saikat Biswas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Koushiki Dasgupta Chaudhuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pabitra Mitra
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Krothapalli Sreenivasa Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Saikat Biswas .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Granada, Granada, Spain

    Olga Valenzuela

  3. University of Granada, Granada, Spain

    Fernando Rojas Ruiz

  4. University of Granada, Granada, Spain

    Luis Javier Herrera

  5. University of Granada, Granada, Spain

    Francisco Ortuño

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Biswas, S., Chaudhuri, K.D., Mitra, P., Rao, K.S. (2023). Relation Predictions in Comorbid Disease Centric Knowledge Graph Using Heterogeneous GNN Models. In: Rojas, I., Valenzuela, O., Rojas Ruiz, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2023. Lecture Notes in Computer Science(), vol 13920. Springer, Cham. https://doi.org/10.1007/978-3-031-34960-7_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-34960-7_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34959-1

  • Online ISBN: 978-3-031-34960-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation