Abstract
Unlike traditional development of new drugs that rely on labor- and time-intensive research and clinical trials, computational approaches, deep learning technologies, in particular, have been prominent in recent research on the topic. By utilizing the ever-growing biomedical knowledge repositories and exploiting the relationship between diverse types of information (e.g., proteins, genes, molecular, diseases, drugs), graph neural networks (GNNs) primed for processing graph-structured data have a real potential for advancing the critical endeavor of drug discovery. Safe and effective drug therapy would also rely on early identification of unwanted and potentially harmful adverse effects a certain drug has on patient’s health. Hence, two, rather contrastive tasks that pertain to the process of drug discovery have been of special interest in this research. The first one is drug repurposing and the second one, a closely-related task of identifying drugs that have an adverse or negative effect on patient health namely drug-induced diseases. In this research, the task of discovering new links between drugs and diseases has been formalized as a link prediction task in a heterogenous graph. The predictive models for drug discovery proposed in this paper were tested on the ogbl-biokg (https://ogb.stanford.edu/docs/linkprop/#ogbl-biokg) dataset from the collection of large benchmark dataset Open Graph Benchmark (OGB) [15]. The openness and multi-source heterogeneity of the OGB dataset has provided us with an opportunity to experiment with HinSage [28], a method for inductive representational learning in heterogenous graphs. Two models based on HinSage, have been proposed proving their superior performance when compared with more traditional similarity-based baseline methods. Furthermore, a selected newly discovered relationship with a potential for drug repurposing has been discussed through the lenses of related clinical-experimental trials.
Supported by Faculty of Computer Science and Engineering, Skopje, N. Macedonia.
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References
An explainable framework for drug repositioning from disease information network. Neurocomputing 511, 247–258 (2022)
Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003)
Ahmed, T., Sklaroff, R., Yagoda, A.: Sequential trials of methotrexate, cisplatin and bleomycin for penile cancer. J. Urol. 132(3), 465–468 (1984)
Al-Rabeah, M.H., Lakizadeh, A.: Prediction of drug-drug interaction events using graph neural networks based feature extraction. Sci. Rep. 12(1) (2022)
Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn. 30(7), 1145–1159 (1997)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Buxton, E., et al.: Combination bleomycin, ifosfamide, and cisplatin chemotherapy in cervical cancer. JNCI: J. Natl. Cancer Inst. 81(5), 359–361 (1989)
Cai, L., et al.: Drug repositioning based on the heterogeneous information fusion graph convolutional network. Briefings Bioinform. 22(6), bbab319 (2021)
Cassinotti, A., et al.: New onset of atrial fibrillation after introduction of azathioprine in ulcerative colitis: case report and review of the literature. Eur. J. Clin. Pharmacol. 63(9), 875–878 (2007)
Chen, F., Wang, Y.C., Wang, B., Kuo, C.C.J.: Graph representation learning: a survey. APSIPA Trans. Signal Inf. Process. 9, e15 (2020)
Dong, Y., Chawla, N.V., Swami, A.: metapath2vec: scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2017)
Hakenberg, O.W., Nippgen, J.B., Froehner, M., Zastrow, S., Wirth, M.P.: Cisplatin, methotrexate and bleomycin for treating advanced penile carcinoma. BJU Int. 98(6), 1225–1227 (2006)
Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Han, X., Xie, R., Li, X., Li, J.: SmileGNN: drug-drug interaction prediction based on the smiles and graph neural network. Life 12(2), 319 (2022)
Hu, W., et al.: Open graph benchmark: datasets for machine learning on graphs. In: Advances in Neural Information Processing Systems, vol. 33, pp. 22118–22133 (2020)
Ioannidis, V.N., Zheng, D., Karypis, G.: Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing. arXiv preprint arXiv:2007.10261 (2020)
Janocha, K., Czarnecki, W.M.: On loss functions for deep neural networks in classification. arXiv preprint arXiv:1702.05659 (2017)
Jin, S., et al.: HeTDR: drug repositioning based on heterogeneous networks and text mining. Patterns 2(8), 100307 (2021)
Lee, J., et al.: BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4), 1234–1240 (2020)
Newman, M.E.: Clustering and preferential attachment in growing networks. Phys. Rev. E 64(2), 025102 (2001)
Niwattanakul, S., Singthongchai, J., Naenudorn, E., Wanapu, S.: Using of Jaccard coefficient for keywords similarity. In: Proceedings of the International Multiconference of Engineers and Computer Scientists, vol. 1, pp. 380–384 (2013)
Pushpakom, S., et al.: Drug repurposing: progress, challenges and recommendations. Nat. Rev. Drug Discov. 18(1) (2019)
Ramachandran, P., Zoph, B., Le, Q.V.: Searching for activation functions. arXiv preprint arXiv:1710.05941 (2017)
Ridings, J.E.: The thalidomide disaster, lessons from the past. In: Barrow, P. (ed.) Teratogenicity Testing. MIMB, vol. 947, pp. 575–586. Springer, Cham (2013). https://doi.org/10.1007/978-1-62703-131-8_36
RxList: Blenoxane (2021). https://www.rxlist.com/blenoxane-drug.htm
Sadeghi, S., Lu, J., Ngom, A.: An integrative heterogeneous graph neural network-based method for multi-labeled drug repurposing. Front. Pharmacol. 13 (2022)
Saitoh, T., et al.: Hodgkin lymphoma presenting with various immunologic abnormalities, including autoimmune hepatitis, hashimoto’s thyroiditis, autoimmune hemolytic anemia, and immune thrombocytopenia. Clin. Lymphoma Myeloma 8(1), 62–64 (2008)
StellarGraph: Heterogeneous graphsage (hinsage) (2020). https://stellargraph.readthedocs.io/en/stable/hinsage.html
Sunghwan, K., et al.: PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res. (2021)
Wang, B., et al.: Similarity network fusion for aggregating data types on a genomic scale. Nat. Methods 11(3), 333–337 (2014)
Wang, Z., Zhou, M., Arnold, C.: Toward heterogeneous information fusion: bipartite graph convolutional networks for in silico drug repurposing. Bioinformatics 36(Supplement_1), i525–i533 (2020)
Weininger, D.: Smiles, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28(1), 31–36 (1988)
Yu, Z., Huang, F., Zhao, X., Xiao, W., Zhang, W.: Predicting drug-disease associations through layer attention graph convolutional network. Briefings Bioinform. 22(4), bbaa243 (2021)
Zeng, X., Zhu, S., Liu, X., Zhou, Y., Nussinov, R., Cheng, F.: deepDR: a network-based deep learning approach to in silico drug repositioning. Bioinformatics 35(24), 5191–5198 (2019)
Zhao, B.W., Hu, L., You, Z.H., Wang, L., Su, X.R.: HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks. Briefings Bioinform. 23(1), bbab515 (2021)
Zhao, B.W., You, Z.H., Wong, L., Zhang, P., Li, H.Y., Wang, L.: MGRL: predicting drug-disease associations based on multi-graph representation learning. Front. Genet. 12, 657182 (2021)
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This work was partially financed by the Faculty of Computer Science and Engineering at the Ss. Cyril and Methodius University in Skopje.
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Trajanoska, M., Toshevska, M., Gievska, S. (2024). Identifying Drug - Disease Interactions Through Link Prediction in Heterogeneous Graphs. In: Mihova, M., Jovanov, M. (eds) ICT Innovations 2023. Learning: Humans, Theory, Machines, and Data. ICT Innovations 2023. Communications in Computer and Information Science, vol 1991. Springer, Cham. https://doi.org/10.1007/978-3-031-54321-0_13
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