The similarity between Disease and Drug Network in Link Prediction

Muhammad Azam; Muhammad Nouman

doi:10.4108/eetmca.v7i2.2667

Authors

Muhammad Azam University of Missouri
Muhammad Nouman University of Agriculture Faisalabad

DOI:

https://doi.org/10.4108/eetmca.v7i2.2667

Keywords:

Social Network Analysis, Link Prediction, Disease, Drug networks

Abstract

Nowadays, data records are being transferred entirely to digital platforms, and data have become embodied and measurable. In this study, to observe the relationship between disease and drug, we first constructed disease and drug networks. These networks consist of a disease diagnosis and drugs written by doctors. After the disease and drug networks were generated, a link prediction was done concerning similarity values between nodes. Experimental results show that the proposed method finds satisfactory results. By examining these constructions and connections it is feasible to achieve affinities, similitudes, and patterns and it is likewise conceivable to make it feasible to achieve.

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References

A. Lakhani, W. Gad, M. Rushdy, and A.-B. M. Salem, “Quackingly: Positive-Unlabelled and Stacking Learning for N-Linked Glycosylation Site Prediction,” IEEE Access, vol. 10, pp. 12702–12713, 2022, DOI: 10.1109/ACCESS.2022.3146395.

M. Frat, A. Kynar, C. Kankava, İ. T. Frat, and T. Tuner, “Prediction of Penta Cam image after corneal cross-linking by linear interpolation technique and U-NET based 2D regression model,” Compute. Biol. Med., vol. 146, p. 105541, Jul. 2022, DOI: 10.1016/j.compbiomed.2022.105541.

L. Cai, J. Li, J. Wang, and S. Ji, “Line Graph Neural Networks for Link Prediction,” IEEE Trans. Pattern Anal. Mach. Intel., pp. 1–1, 2021, DOI: 10.1109/TPAMI.2021.3080635.

Y. Wang and J. Wang, “Is Megamerger Better? –Based on the Link Prediction Perspective,” IEEE Access, vol. 10, pp. 79805–79814, 2022, DOI: 10.1109/ACCESS.2022.3195222.

J. Chen, X. Wang, and X. Xu, “GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Prediction.” arrive Oct. 02, 2021. Accessed: Aug. 29, 2022. [Online]. Available: http://arxiv.org/abs/1812.04206

N. Simmons, S. B. F. Gomes, M. D. Yacoub, O. Simeone, S. L. Cotton, and D. E. Simmons, “AI-Based Channel Prediction in D2D Links: An Empirical Validation,” IEEE Access, vol. 10, pp. 65459–65472, 2022, DOI: 10.1109/ACCESS.2022.3182713.

J. Swarding, M. Helmrich, B. Garbs, and K. Musial, “A Robust Comparative Analysis of Graph Neural Networks on Dynamic Link Prediction,” IEEE Access, vol. 10, pp. 64146–64160, 2022, DOI: 10.1109/ACCESS.2022.3175981.

C. Scholz, M. At Mueller, A. Barat, C. Battuto, and G. Stummel, “New Insights and Methods for Predicting Face-to-Face Contacts,” p. 10.

W. Divesting, M. Peacenik, G. Fletcher, V. Makowski, E. Postma, and J. Vanhorn, “Beeler 2017: Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning,” p. 194.

I. S. Bohai and M. J. van der Lei, “A Social Network Analysis of Occupational Segregation,” ArXiv200409293 Cs Econ, Apr. 2020, Accessed: Apr. 26, 2021. [Online]. Available: http://arxiv.org/abs/2004.09293

A. Samad, M. Qadir, I. Nawaz, and M. A. Islam, “A Comprehensive Survey of Link Prediction Techniques for Social Network,” p. 22.

L. Gunasinghe and R. Incise, “Time-Aware Index for Link Prediction in Social Networks,” p. 2.

K. Zhou, T. P. Michalak, T. Rahwa, M. Warnie, and Y. Verbeeck, “Attacking Similarity-Based Link Prediction in Social Networks,” ArXiv180908368 Cs, Dec. 2018, Accessed: Apr. 26, 2021. [Online]. Available: http://arxiv.org/abs/1809.08368

N. M. A. Ibrahim and L. Chen, “Link prediction in dynamic social networks by integrating different types of information,” Appl. Intel., vol. 42, no. 4, pp. 738–750, Jun. 2015, doi: 10.1007/s10489-014-0631-0.

T. Franz, A. Schultz, S. Sizov, and S. Stab, “Triple Rank: Ranking Semantic Web Data by Tensor Decomposition,” in The Semantic Web - ISWC 2009, vol. 5823, A. Bernstein, D. R. Karger, T. Heath, L. Feigenbaum, D. Maynard, E. Motta, and K. Irigarayan, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 213–228. doi: 10.1007/978-3-642-04930-9_14.

I. Subsieve, J. B. Tenenbaum, and R. R. Salahuddin, “Modelling Relational Data using Bayesian Clustered Tensor Factorization,” p. 8.

L. Lu and T. Zhou, “Link Prediction in Complex Networks: A Survey,” ArXiv10100725 Phys., Oct. 2010, DOI: 10.1016/j.physa.2010.11.027.

A. Clause, C. Moore, and M. E. J. Newman, “Hierarchical structure and the prediction of missing links in networks,” ArXiv08110484 Phys. Q-Bio Stat, Nov. 2008, doi: 10.1038/nature06830.

F. Adara and D. Rinke, “Discovering Missing Links in Wikipedia,” p. 8.

C. C. Aggarwal, Y. Xia, and P. S. Yu, “A framework for dynamic link prediction in heterogeneous networks: Dynamic Link Prediction in Heterogeneous Networks,” Stat. Anal. Data Min. ASA Data Sci. J., vol. 7, no. 1, pp. 14–33, Feb. 2014, DOI: 10.1002/sam.11198.

A. Samad, M. Azam, and M. Qadir, “Structural Importance-based Link Prediction Techniques in Social Network,” EAI Endorsed Trans. Ind. Newt. Intel. Syst., vol. 7, no. 25, p. 167840, Jan. 2021, doi: 10.4108/eai.7-1-2021.167840.

M. Azam, M. Nouman, and A. R. Gill, “Comparative Analysis of Machine Learning techniques to Improve Software Defect Prediction,” Inf. Sci., vol. 5, no. 2, p. 26.

I. Balaenid, C. Allen, and T. M. Hopedale’s, “Tucker: Tensor Factorization for Knowledge Graph Completion,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019, pp. 5184–5193. DOI: 10.18653/v1/D19-1522.

K. Ji, B. Hui, and G. Luo, “Graph Attention Networks with Local Structure Awareness for Knowledge Graph Completion,” IEEE Access, vol. 8, pp. 224860–224870, 2020, DOI: 10.1109/ACCESS.2020.3044343.

S. M. Kazem and D. Poole, “Simple Embedding for Link Prediction in Knowledge Graphs,” p. 12.

F. Akami, M. S. Saeed, Q. Zhang, W. Hu, and C. Li, “Realistic Re-evaluation of Knowledge Graph Completion Methods: An Experimental Study,” in Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, Portland OR USA, Jun. 2020, pp. 1995–2010. DOI: 10.1145/3318464.3380599.

X. Liu, H. Tan, Q. Chen, and G. Lin, “RAGAT: Relation Aware Graph Attention Network for Knowledge Graph Completion,” IEEE Access, vol. 9, pp. 20840–20849, 2021, DOI: 10.1109/ACCESS.2021.3055529.

M. Zhang and Y. Chen, “Link Prediction Based on Graph Neural Networks,” p. 11.

K. Shang, M. Small, and W. Yan, “Link direction for link prediction,” Phys. Stat. Mech. Its Appl., vol. 469, pp. 767–776, Mar. 2017, DOI: 10.1016/j.physa.2016.11.129.

V. Martínez, F. Bernal, and J.-C. Cubero, “A Survey of Link Prediction in Complex Networks,” ACM Compute. Surd., vol. 49, no. 4, pp. 1–33, Dec. 2017, doi: 10.1145/3012704.

H. Wang, H. Ren, and J. Lekovic, “Entity Context and Relational Paths for knowledge Graph Completion,” p. 13.

A. Shahmohammadi, E. Khatanga, and A. Bagheri, “Presenting new collaborative link prediction methods for activity recommendation in Facebook,” p. 10, 2016.

D. Leben-Nowell and J. Kleinberg, “The link-prediction problem for social networks,” J. Am. Soc. Inf. Sci. Technol., p. 13, 2007.