A Scoping Review of Knowledge Graphs in Pharmacovigilance: Reference Sheet and Material

This page contains the list of references used in our scoping review. Additional details can be found on this spreadsheet, which can be used for further analysis of the references.

Please open an issue if you would like to suggest new references.

Our scoping review article got accepted on June 5, 2024 and is now available online. To cite:

@misc{Hauben2024,
  author    = {Manfred Hauben and
               Mazin Rafi and
               Ibrahim Abdelaziz and
               Oktie Hassanzadeh},
  title = {Knowledge Graphs in Pharmacovigilance: A Scoping Review},
  journal = {Clinical Therapeutics},
  year = {2024},
  issn = {0149-2918},
  doi = {https://doi.org/10.1016/j.clinthera.2024.06.003},
}

Reference List

1.

Zhang J, Chen M, Liu J, et al. A Knowledge-Graph-Based Multimodal Deep Learning Framework for Identifying Drug–Drug Interactions. Molecules. 2023;28(3):1490. doi:10.3390/molecules28031490

2.

Asada M, Miwa M, Sasaki Y. Integrating heterogeneous knowledge graphs into drug–drug interaction extraction from the literature. Bioinformatics. 2023;39(1):btac754. doi:10.1093/bioinformatics/btac754

3.

Sakor A, Jozashoori S, Niazmand E, et al. Knowledge4COVID-19: A semantic-based approach for constructing a COVID-19 related knowledge graph from various sources and analyzing treatments’ toxicities. Journal of Web Semantics. 2023;75:100760. doi:10.1016/j.websem.2022.100760

4.

Chen YH, Shih YT, Chien CS, Tsai CS. Predicting adverse drug effects: A heterogeneous graph convolution network with a multi-layer perceptron approach. PLOS ONE. 2022;17(12):e0266435. doi:10.1371/journal.pone.0266435

5.

Hong Y, Luo P, Jin S, Liu X. LaGAT: link-aware graph attention network for drug–drug interaction prediction. Bioinformatics. 2022;38(24):5406-5412. doi:10.1093/bioinformatics/btac682

6.

Al-Rabeah MH, Lakizadeh A. Prediction of drug-drug interaction events using graph neural networks based feature extraction. Sci Rep. 2022;12(1):15590. doi:10.1038/s41598-022-19999-4

7.

Ren ZH, You ZH, Yu CQ, et al. A biomedical knowledge graph-based method for drug–drug interactions prediction through combining local and global features with deep neural networks. Briefings in Bioinformatics. 2022;23(5):bbac363. doi:10.1093/bib/bbac363

8.

Liu Z, Gao X, Li C. Modeling COVID-19 Vaccine Adverse Effects with a Visualized Knowledge Graph Database. Healthcare. 2022;10(8):1419. doi:10.3390/healthcare10081419

9.

Lukashina N, Kartysheva E, Spjuth O, Virko E, Shpilman A. SimVec: predicting polypharmacy side effects for new drugs. Journal of Cheminformatics. 2022;14(1):49. doi:10.1186/s13321-022-00632-5

10.

Joshi P, V M, Mukherjee A. A knowledge graph embedding based approach to predict the adverse drug reactions using a deep neural network. Journal of Biomedical Informatics. 2022;132:104122. doi:10.1016/j.jbi.2022.104122

11.

Chen M, Jiang W, Pan Y, Dai J, Lei Y, Ji C. SGFNNs: Signed Graph Filtering-based Neural Networks for Predicting Drug-Drug Interactions. J Comput Biol. 2022;29(10):1104-1116. doi:10.1089/cmb.2022.0113

12.

He C, Liu Y, Li H, et al. Multi-type feature fusion based on graph neural network for drug-drug interaction prediction. BMC Bioinformatics. 2022;23(1):224. doi:10.1186/s12859-022-04763-2

13.

Yu L, Cheng M, Qiu W, Xiao X, Lin W. idse-HE: Hybrid embedding graph neural network for drug side effects prediction. Journal of Biomedical Informatics. 2022;131:104098. doi:10.1016/j.jbi.2022.104098

14.

He H, Chen G, Yu-Chian Chen C. 3DGT-DDI: 3D graph and text based neural network for drug–drug interaction prediction. Briefings in Bioinformatics. 2022;23(3):bbac134. doi:10.1093/bib/bbac134

15.

Su X, Hu L, You Z, Hu P, Zhao B. Attention-based Knowledge Graph Representation Learning for Predicting Drug-drug Interactions. Briefings in Bioinformatics. 2022;23(3):bbac140. doi:10.1093/bib/bbac140

16.

Feng YH, Zhang SW, Zhang QQ, Zhang CH, Shi JY. deepMDDI: A deep graph convolutional network framework for multi-label prediction of drug-drug interactions. Analytical Biochemistry. 2022;646:114631. doi:10.1016/j.ab.2022.114631

17.

Ren ZH, Yu CQ, Li LP, et al. BioDKG–DDI: predicting drug–drug interactions based on drug knowledge graph fusing biochemical information. Briefings in Functional Genomics. 2022;21(3):216-229. doi:10.1093/bfgp/elac004

18.

Hao X, Chen Q, Pan H, et al. Enhancing drug–drug interaction prediction by three-way decision and knowledge graph embedding. Granul Comput. 2023;8(1):67-76. doi:10.1007/s41066-022-00315-4

19.

Xu X, Yue L, Li B, et al. DSGAT: predicting frequencies of drug side effects by graph attention networks. Briefings in Bioinformatics. 2022;23(2):bbab586. doi:10.1093/bib/bbab586

20.

Yao J, Sun W, Jian Z, Wu Q, Wang X. Effective knowledge graph embeddings based on multidirectional semantics relations for polypharmacy side effects prediction. Bioinformatics. 2022;38(8):2315-2322. doi:10.1093/bioinformatics/btac094

21.

Liu Z, Wang XN, Yu H, Shi JY, Dong WM. Predict multi-type drug–drug interactions in cold start scenario. BMC Bioinformatics. 2022;23(1):75. doi:10.1186/s12859-022-04610-4

22.

Han X, Xie R, Li X, Li J. SmileGNN: Drug–Drug Interaction Prediction Based on the SMILES and Graph Neural Network. Life. 2022;12(2):319. doi:10.3390/life12020319

23.

Wang F, Lei X, Liao B, Wu FX. Predicting drug–drug interactions by graph convolutional network with multi-kernel. Briefings in Bioinformatics. 2022;23(1):bbab511. doi:10.1093/bib/bbab511

24.

Wang N, Cai X, Yang L, Mei X. Safe medicine recommendation via star interactive enhanced-based transformer model. Computers in Biology and Medicine. 2022;141:105159. doi:10.1016/j.compbiomed.2021.105159

25.

Dasgupta S, Jayagopal A, Hong ALJ, Mariappan R, Rajan V. Adverse Drug Event Prediction Using Noisy Literature-Derived Knowledge Graphs: Algorithm Development and Validation. JMIR Medical Informatics. 2021;9(10):e32730. doi:10.2196/32730

26.

Dai Y, Guo C, Guo W, Eickhoff C. Drug–drug interaction prediction with Wasserstein Adversarial Autoencoder-based knowledge graph embeddings. Briefings in Bioinformatics. 2021;22(4):bbaa256. doi:10.1093/bib/bbaa256

27.

Wang M, Wang H, Liu X, Ma X, Wang B. Drug-Drug Interaction Predictions via Knowledge Graph and Text Embedding: Instrument Validation Study. JMIR Med Inform. 2021;9(6):e28277. doi:10.2196/28277

28.

Bresso E, Monnin P, Bousquet C, et al. Investigating ADR mechanisms with Explainable AI: a feasibility study with knowledge graph mining. BMC Medical Informatics and Decision Making. 2021;21(1):171. doi:10.1186/s12911-021-01518-6

29.

Yu Y, Huang K, Zhang C, Glass LM, Sun J, Xiao C. SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization. Bioinformatics. 2021;37(18):2988-2995. doi:10.1093/bioinformatics/btab207

30.

Bang S, Jhee JH, Shin H. Polypharmacy side-effect prediction with enhanced interpretability based on graph feature attention network. Bioinformatics. 2021;37(18):2955-2962. doi:10.1093/bioinformatics/btab174

31.

Gong F, Wang M, Wang H, Wang S, Liu M. SMR: Medical Knowledge Graph Embedding for Safe Medicine Recommendation. Big Data Research. 2021;23:100174. doi:10.1016/j.bdr.2020.100174

32.

Zhang F, Sun B, Diao X, Zhao W, Shu T. Prediction of adverse drug reactions based on knowledge graph embedding. BMC Medical Informatics and Decision Making. 2021;21(1):38. doi:10.1186/s12911-021-01402-3

33.

Wang M, Ma X, Si J, et al. Adverse Drug Reaction Discovery Using a Tumor-Biomarker Knowledge Graph. Frontiers in Genetics. 2021;11. Accessed May 12, 2023. https://www.frontiersin.org/articles/10.3389/fgene.2020.625659

34.

Nováček V, Mohamed SK. Predicting Polypharmacy Side-effects Using Knowledge Graph Embeddings. AMIA Jt Summits Transl Sci Proc. 2020;2020:449-458. Accessed May 12, 2023. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233093/

35.

Xue R, Liao J, Shao X, et al. Prediction of Adverse Drug Reactions by Combining Biomedical Tripartite Network and Graph Representation Model. Chem Res Toxicol. 2020;33(1):202-210. doi:10.1021/acs.chemrestox.9b00238

36.

Celebi R, Uyar H, Yasar E, Gumus O, Dikenelli O, Dumontier M. Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings. BMC Bioinformatics. 2019;20(1):726. doi:10.1186/s12859-019-3284-5

37.

Shtar G, Rokach L, Shapira B. Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures. PLOS ONE. 2019;14(8):e0219796. doi:10.1371/journal.pone.0219796

38.

Muñoz E, Nováček V, Vandenbussche PY. Facilitating prediction of adverse drug reactions by using knowledge graphs and multi-label learning models. Briefings in Bioinformatics. 2019;20(1):190-202. doi:10.1093/bib/bbx099

39.

Zitnik M, Agrawal M, Leskovec J. Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics. 2018;34(13):i457-i466. doi:10.1093/bioinformatics/bty294

40.

Bean DM, Wu H, Iqbal E, et al. Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records. Sci Rep. 2017;7(1):16416. doi:10.1038/s41598-017-16674-x

41.

Abdelaziz I, Fokoue A, Hassanzadeh O, Zhang P, Sadoghi M. Large-scale structural and textual similarity-based mining of knowledge graph to predict drug–drug interactions. Journal of Web Semantics. 2017;44:104-117. doi:10.1016/j.websem.2017.06.002

42.

Jiang G, Liu H, Solbrig HR, Chute CG. Mining severe drug-drug interaction adverse events using Semantic Web technologies: a case study. BioData Min. 2015;8:12. doi:10.1186/s13040-015-0044-6

43.

Noor A, Assiri A, Ayvaz S, Clark C, Dumontier M. Drug-drug interaction discovery and demystification using Semantic Web technologies. J Am Med Inform Assoc. 2017;24(3):556-564. doi:10.1093/jamia/ocw128

44.

Zhang W, Chen Y, Liu F, Luo F, Tian G, Li X. Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data. BMC Bioinformatics. 2017;18(1):18. doi:10.1186/s12859-016-1415-9

45.

Zhao H, Zheng K, Li Y, Wang J. A novel graph attention model for predicting frequencies of drug–side effects from multi-view data. Briefings in Bioinformatics. 2021;22(6):bbab239. doi:10.1093/bib/bbab239

46.

Hu B, Wang H, Yu Z. Drug Side-Effect Prediction Via Random Walk on the Signed Heterogeneous Drug Network. Molecules. 2019;24(20):3668. doi:10.3390/molecules24203668

47.

Kwak H, Lee M, Yoon S, Chang J, Park S, Jung K. Drug-Disease Graph: Predicting Adverse Drug Reaction Signals via Graph Neural Network with Clinical Data. Advances in Knowledge Discovery and Data Mining. 2020;12085:633-644. doi:10.1007/978-3-030-47436-2_48

Other Relevant Articles

1.

Ji S, Gao Y, Marttinen P. Knowledge-augmented Graph Neural Networks with Concept-aware Attention for Adverse Drug Event Detection. Published online January 25, 2023. doi:10.48550/arXiv.2301.10451

2.

Galeano D, Li S, Gerstein M, Paccanaro A. Predicting the frequencies of drug side effects. Nat Commun. 2020;11(1):4575. doi:10.1038/s41467-020-18305-y

3.

Ye Q, Hsieh CY, Yang Z, et al. A unified drug–target interaction prediction framework based on knowledge graph and recommendation system. Nat Commun. 2021;12(1):6775. doi:10.1038/s41467-021-27137-3

4.

Xu X, Meng F, Sun L. Knowledge Mining of Interactions between Drugs from the Extensive Literature with a Novel Graph-Convolutional-Network-Based Method. Electronics. 2023;12(2):311. doi:10.3390/electronics12020311

5.

Li J, Yang X, Guan Y, Pan Z. Prediction of Drug-Target Interaction Using Dual-Network Integrated Logistic Matrix Factorization and Knowledge Graph Embedding. Molecules. 2022;27(16):5131. doi:10.3390/molecules27165131

6.

Alshahrani M, Almansour A, Alkhaldi A, et al. Combining biomedical knowledge graphs and text to improve predictions for drug-target interactions and drug-indications. PeerJ. 2022;10:e13061. doi:10.7717/peerj.13061

7.

Huang L, Fernandes H, Zia H, et al. The cancer precision medicine knowledge base for structured clinical-grade mutations and interpretations. Journal of the American Medical Informatics Association. 2017;24(3):513-519. doi:10.1093/jamia/ocw148

8.

Shen C, Li Z, Chu Y, Zhao Z. GAR: Graph adversarial representation for adverse drug event detection on Twitter. Applied Soft Computing. 2021;106:107324. doi:10.1016/j.asoc.2021.107324

9.

Symeonidis P, Chairistanidis S, Zanker M. Safe, effective and explainable drug recommendation based on medical data integration. User Modeling and User-Adapted Interaction. 2022;32(5):999-1018. doi:10.1007/s11257-022-09342-x