DRP-VEM: Drug repositioning using voting ensemble model

Document Type : Original Article

Authors

Computational Biology Research Center (CBRC), Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran

Abstract

Conventional approaches to drug discovery are both expensive and time-intensive. To circumvent these challenges, drug repurposing or repositioning (DR) has emerged as a prevalent strategy. A noteworthy advancement in this field involves the widespread application of machine learning techniques. The effectiveness of these methods depends on the quality of features, their representations, and the underlying dataset. Notably, the issue of redundancy in feature sets can detrimentally impact the overall performance of these methods. Furthermore, the careful selection of a suitable training set plays a pivotal role in enhancing the accuracy of machine learning approaches in addressing drug repurposing challenges. Discovering the appropriate training set faces two significant challenges. Firstly, many methods utilize known drug-disease pairs for positives and unknown pairs for negatives. The stark imbalance in the number of known and unknown pairs often results in a bias towards the larger group, introducing errors in machine learning performance. Secondly, the absence of a documented drug-disease association indicates that it hasn't been experimentally approved yet, and this status may change in the future. This paper introduces DRP-VEM, a novel approach designed for predicting drug repositioning, specifically customized to tackle the challenges previously outlined. DRP-VEM evaluates the effectiveness of binary-based and similarity-based representations of drugs and diseases in enhancing the model's performance. Additionally, it proposes a voting ensemble training strategy, adept at managing imbalanced datasets. The assessment of DRP-VEM spans a range of parameters, including its efficacy in representing both diseases and drugs, the proficiency of its classification methods, and the application of voting ensemble training approaches using heterogeneous evaluation criteria. Significantly, DRP-VEM achieves an AUC-ROC of 81.8\% and AUC-PR of 76.6\%. Comparative analysis with other studies highlights the superior performance of the proposed model, underscoring its effectiveness in drug repositioning prediction.

Keywords

Main Subjects

References

[1] T. T. Ashburn and K. B. Thor, Drug repositioning: identifying and developing new uses for existing drugs, Nature Reviews Drug Discovery, 3 (2004), pp. 673–683.
[2] A. S. Brown and C. J. Patel, A standard database for drug repositioning, Scientific Data, 4 (2017), p. 170029.
[3] H. Chen, Z. Zhang, and J. Zhang, In silico drug repositioning based on the integration of chemical, genomicand pharmacological spaces, BMC Bioinformatics, 22 (2021), p. 52.
[4] L. Cheng, Y. Hu, J. Sun, M. Zhou, and Q. Jiang, Dincrna: a comprehensive web-based bioinformaticstoolkit for exploring disease associations and ncrna function, Bioinformatics, 34 (2018), pp. 1953–1956.
[5] A. Chiang and A. Butte, Systematic evaluation of drug–disease relationships to identify leads for novel drug uses, Clinical Pharmacology & Therapeutics, 86 (2009), pp. 507–510.
[6] T. U. Consortium, Uniprot: the universal protein knowledgebase in 2021, Nucleic Acids Research, 49 (2020), pp. D480–D489.
[7] J. T. Dudley, T. Deshpande, and A. J. Butte, Exploiting drug–disease relationships for computationaldrug repositioning, Briefings in Bioinformatics, 12 (2011), pp. 303–311.
[8] C. Harrison, Coronavirus puts drug repurposing on the fast track, Nature biotechnology, 38 (2020), pp. 379– 381.
[9] S. Kim, J. Chen, T. Cheng, A. Gindulyte, J. He, S. He, Q. Li, B. A. Shoemaker, P. A. Thiessen, B. Yu, L. Zaslavsky, J. Zhang, and E. E. Bolton, Pubchem 2019 update: improved access to chemical data, Nucleic Acids Research, 47 (2018), pp. D1102–D1109.
[10] B. Krawczyk, Learning from imbalanced data: open challenges and future directions, Progress in ArtificialIntelligence, 5 (2016), pp. 221–232.
[11] M. Kuhn, I. Letunic, L. J. Jensen, and P. Bork, The sider database of drugs and side effects, Nucleic Acids Research, 44 (2015), pp. D1075–D1079.
[12] R. Kumar and A. Indrayan, Receiver operating characteristic (roc) curve for medical researchers, Indian Pediatrics, 48 (2011), pp. 277–287. 
[13] H. Luo, J. Wang, M. Li, J. Luo, P. Ni, K. Zhao, F.-X. Wu, and Y. Pan, Computational drug repositioning with random walk on a heterogeneous network, IEEE/ACM Transactions on Computational Biologyand Bioinformatics, 16 (2019), pp. 1890–1900.
[14] M. Moridi, M. Ghadirinia, A. Sharifi-Zarchi, and F. Zare-Mirakabad, The assessment of efficient representation of drug features using deep learning for drug repositioning, BMC Bioinformatics, 20 (2019), p. 577.
[15] M. G. Ozsoy, T. Ozyer, F. Polat, and R. Alhajj ¨ , Realizing drug repositioning by adapting a recommendation system to handle the process, BMC Bioinformatics, 19 (2018), p. 136.
[16] H. R. Sofaer, J. A. Hoeting, and C. S. Jarnevich, The area under the precision-recall curve as aperformance metric for rare binary events, Methods in Ecology and Evolution, 10 (2019), pp. 565–577.
[17] D. Wang, J. Wang, M. Lu, F. Song, and Q. Cui, Inferring the human microrna functional similarity andfunctional network based on microrna-associated diseases, Bioinformatics, 26 (2010), pp. 1644–1650.
[18] D. S. Wishart, Y. D. Feunang, A. C. Guo, E. J. Lo, A. Marcu, J. R. Grant, T. Sajed, D. Johnson, C. Li, Z. Sayeeda, N. Assempour, I. Iynkkaran, Y. Liu, A. Maciejewski, N. Gale, A. Wilson, L. Chin, R. Cummings, D. Le, A. Pon, C. Knox, and M. Wilson, Drugbank 5.0: a major update to the drugbank database for 2018, Nucleic Acids Research, 46 (2017), pp. D1074–D1082.
[19] O. J. Wouters, M. McKee, and J. Luyten, Estimated research and development investment needed tobring a new medicine to market, 2009-2018, JAMA, 323 (2020), pp. 844–853.
[20] P. Xuan, Y. Cao, T. Zhang, X. Wang, S. Pan, and T. Shen, Drug repositioning through integration ofprior knowledge and projections of drugs and diseases, Bioinformatics, 35 (2019), pp. 4108–4119.
[21] X. Zeng, S. Zhu, X. Liu, Y. Zhou, R. Nussinov, and F. Cheng, deepdr: a network-based deep learning approach to in silico drug repositioning, Bioinformatics, 35 (2019), pp. 5191–5198.
AUT Journal of Mathematics and Computing
Volume 6, Issue 4
2025
Pages 297-310

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