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İlaç - İlaç Etkileşimi Tahmini için Konvolüsyonel Sinir Ağı Tabanlı Yeni Bir Yaklaşım

Year 2023, Volume: 27 Issue: 1, 137 - 144, 25.04.2023

Ramazan Özgür Doğan Hülya Doğan Feride Sena Sezen

https://doi.org/10.19113/sdufenbed.1182333

Abstract

Aynı anda birden fazla ilaç kullanımında özellikle son yıllarda büyük artış görülmektedir. Bu durum ilaçlar arası reaksiyon olarak tanımlanan ilaç – ilaç etkileşimlerine yol açabilmektedir. Hastalarda oluşabilecek olumsuz durumların engellenmesi için ilaçlar arasındaki etkileşimlerin tahmin edilmesi gerekmektedir. İlaç – ilaç etkileşimlerinin tahmini genelde deneyler ile gerçekleştirmekte ve yoğun iş yükü gerektirmektedir. Klinisyenlerin daha doğru kararlar alması ve uygun tedavi programları oluşturması için literatürde otomatik ilaç – ilaç etkileşimi tahmini gerçekleştiren yaklaşımlar sıklıkla gerçekleştirilmiştir. Literatürde ilaç – ilaç etkileşimi tahmini için birçok çalışma geliştirilmesine rağmen, bu alanda hala belirgin kısıtlamalar mevcuttur. İlaç – ilaç etkileşimi tahmini alanında karşılaşılan kısıtlamaları minimize etmek amacıyla bu çalışmada ilaçların yapısal özellikleri kullanılarak literatürdeki çalışmalardan daha gelişmiş konvolüsyon sinir ağı modeli önerilmektedir. Önerilen yaklaşım, özellik çıkarma ve konvolüsyon sinir ağı modelinin tasarımı olmak üzere iki ana aşamada gerçekleştirilmektedir. Çalışmada kullanılan performans değerlendirme prosedürleri açısından, önerilen yaklaşımın başarısının ilaç – ilaç etkileşimi tahmini için tatmin edici olduğu açıkça görülmektedir.

Keywords

İlaç - ilaç etkileşimi Konvolüsyonel sinir ağları Derin öğrenme

References

  • [1] Patton, K., Borshoff, D. C. 2018. Adverse drug reactions. Anaesthesia, 73, 76-84.
  • [2] Niu, J., Straubinger, R. M., Mager, D. E. 2019. Pharmacodynamic drug–drug interactions. Clinical Pharmacology & Therapeutics, 105(6), 1395-1406.
  • [3] Zhang, T., Leng, J., Liu, Y. 2020. Deep learning for drug–drug interaction extraction from the literature: a review. Briefings in bioinformatics, 21(5), 1609-1627.
  • [4] Han, K., Cao, P., Wang, Y., Xie, F., Ma, J., Yu, M., ... & Wan, J. 2021. A Review of Approaches for Predicting Drug-Drug Interactions Based on Machine Learning. Frontiers in Pharmacology, 12, 814858-814858.
  • [5] Sridhar, D., Fakhraei, S., Getoor, L. 2016. A probabilistic approach for collective similarity-based drug–drug interaction prediction. Bioinformatics, 32(20), 3175-3182.
  • [6] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P. 2016, May. Predicting drug-drug interactions through large-scale similarity-based link prediction. In European Semantic Web Conference, pp. 774-789. Springer, Cham.
  • [7] Ferdousi, R., Safdari, R., Omidi, Y. 2017. Computational prediction of drug-drug interactions based on drugs functional similarities. Journal of biomedical informatics, 70, 54-64.
  • [8] Zheng, Y., Peng, H., Zhang, X., Zhao, Z., Gao, X., Li, J. 2019. DDI-PULearn: a positive-unlabeled learning method for large-scale prediction of drug-drug interactions. BMC bioinformatics, 20(19), 1-12.
  • [9] Song, D., Chen, Y., Min, Q., Sun, Q., Ye, K., Zhou, C., ... & Liao, J. 2019. Similarity‐based machine learning support vector machine predictor of drug‐drug interactions with improved accuracies. Journal of clinical pharmacy and therapeutics, 44(2), 268-275.
  • [10] Ibrahim, H., El Kerdawy, A. M., Abdo, A., Eldin, A. S. 2021. Similarity-based machine learning framework for predicting safety signals of adverse drug–drug interactions. Informatics in Medicine Unlocked, 26, 100699.
  • [11] Yan, C., Duan, G., Zhang, Y., Wu, F. X., Pan, Y., Wang, J. 2020. Predicting drug-drug interactions based on integrated similarity and semi-supervised learning. IEEE/ACM Transactions on Computational Biology and Bioinformatics.
  • [12] Shi, J. Y., Huang, H., Li, J. X., Lei, P., Zhang, Y. N., Yiu, S. M. 2017, April. Predicting comprehensive drug-drug interactions for new drugs via triple matrix factorization. In International Conference on Bioinformatics and Biomedical Engineering, pp. 108-117. Springer, Cham.
  • [13] Zhang, W., Chen, Y., Li, D., Yue, X. 2018. Manifold regularized matrix factorization for drug-drug interaction prediction. Journal of biomedical informatics, 88, 90-97.
  • [14] Shi, J. Y., Huang, H., Li, J. X., Lei, P., Zhang, Y. N., Dong, K., Yiu, S. M. 2018. TMFUF: a triple matrix factorization-based unified framework for predicting comprehensive drug-drug interactions of new drugs. BMC bioinformatics, 19(14), 27-37.
  • [15] Shtar, G., Rokach, L., Shapira, B. 2019. Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures. PloS one, 14(8), e0219796.
  • [16] Rohani, N., Eslahchi, C., Katanforoush, A. 2020. Iscmf: Integrated similarity-constrained matrix factorization for drug–drug interaction prediction. Network Modeling Analysis in Health Informatics and Bioinformatics, 9(1), 1-8.
  • [17] Liu, S., Tang, B., Chen, Q., Wang, X. 2016. Drug-drug interaction extraction via convolutional neural networks. Computational and mathematical methods in medicine, 2016.
  • [18] Asada, M., Miwa, M., Sasaki, Y. 2017, August. Extracting drug-drug interactions with attention CNNs. In BioNLP 2017, pp. 9-18.
  • [19] Suárez-Paniagua, V., Segura-Bedmar, I. 2018. Evaluation of pooling operations in convolutional architectures for drug-drug interaction extraction. BMC bioinformatics, 19(8), 39-47.
  • [20] Wu, H., Xing, Y., Ge, W., Liu, X., Zou, J., Zhou, C., Liao, J. 2020. Drug-drug interaction extraction via hybrid neural networks on biomedical literature. Journal of biomedical informatics, 106, 103432.
  • [21] Deng, Y., Xu, X., Qiu, Y., Xia, J., Zhang, W., Liu, S. 2020. A multimodal deep learning framework for predicting drug–drug interaction events. Bioinformatics, 36(15), 4316-4322.
  • [22] Zhang, C., Lu, Y., Zang, T. 2022. CNN-DDI: a learning-based method for predicting drug–drug interactions using convolution neural networks. BMC bioinformatics, 23(1), 1-12.
  • [23] Feng, Y. H., Zhang, S. W., Zhang, Q. Q., Zhang, C. H., Shi, J. Y. 2022. deepMDDI: A deep graph convolutional network framework for multi-label prediction of drug-drug interactions. Analytical Biochemistry, 646, 114631.
  • [24] Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., ... & Wilson, M. 2018. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic acids research, 46(D1), D1074-D1082.

A Novel Convolutional Neural Network-based Approach for Prediction of Drug - Drug Interaction

Year 2023, Volume: 27 Issue: 1, 137 - 144, 25.04.2023

Ramazan Özgür Doğan Hülya Doğan Feride Sena Sezen

https://doi.org/10.19113/sdufenbed.1182333

Abstract

There has been a significant increase in the use of more than one drug, especially in recent years. Concomitant use of medications by a patient can lead to drug-drug interactions, which are defined as drug-to-drug reactions. In order to prevent adverse situations, it is necessary to predict the interactions that may occur between drugs. The prediction of drug-drug interactions is usually carried out with experiments and requires an intense workload. In order for clinicians to make more accurate decisions and create appropriate treatment programs, approaches that perform automatic prediction of drug-drug interaction have been frequently used in the literature. Although many studies have been developed in the literature for prediction of drug-drug interaction, there are still significant limitations in this area. In order to minimize the limitations encountered in prediction of drug-drug interaction, this study proposes a more advanced convolution neural network model than the studies in the literature, using the properties of drugs. The proposed approach is carried out in two main stages, feature extraction and design of the convolutional neural network model. In terms of results obtained with performance evaluation procedures, it is clear that the success of the proposed approach is superior to other approaches for prediction of drug-drug interaction.

Keywords

Drug – drug interaction Convolutional neural network Deep learning

References

  • [1] Patton, K., Borshoff, D. C. 2018. Adverse drug reactions. Anaesthesia, 73, 76-84.
  • [2] Niu, J., Straubinger, R. M., Mager, D. E. 2019. Pharmacodynamic drug–drug interactions. Clinical Pharmacology & Therapeutics, 105(6), 1395-1406.
  • [3] Zhang, T., Leng, J., Liu, Y. 2020. Deep learning for drug–drug interaction extraction from the literature: a review. Briefings in bioinformatics, 21(5), 1609-1627.
  • [4] Han, K., Cao, P., Wang, Y., Xie, F., Ma, J., Yu, M., ... & Wan, J. 2021. A Review of Approaches for Predicting Drug-Drug Interactions Based on Machine Learning. Frontiers in Pharmacology, 12, 814858-814858.
  • [5] Sridhar, D., Fakhraei, S., Getoor, L. 2016. A probabilistic approach for collective similarity-based drug–drug interaction prediction. Bioinformatics, 32(20), 3175-3182.
  • [6] Fokoue, A., Sadoghi, M., Hassanzadeh, O., Zhang, P. 2016, May. Predicting drug-drug interactions through large-scale similarity-based link prediction. In European Semantic Web Conference, pp. 774-789. Springer, Cham.
  • [7] Ferdousi, R., Safdari, R., Omidi, Y. 2017. Computational prediction of drug-drug interactions based on drugs functional similarities. Journal of biomedical informatics, 70, 54-64.
  • [8] Zheng, Y., Peng, H., Zhang, X., Zhao, Z., Gao, X., Li, J. 2019. DDI-PULearn: a positive-unlabeled learning method for large-scale prediction of drug-drug interactions. BMC bioinformatics, 20(19), 1-12.
  • [9] Song, D., Chen, Y., Min, Q., Sun, Q., Ye, K., Zhou, C., ... & Liao, J. 2019. Similarity‐based machine learning support vector machine predictor of drug‐drug interactions with improved accuracies. Journal of clinical pharmacy and therapeutics, 44(2), 268-275.
  • [10] Ibrahim, H., El Kerdawy, A. M., Abdo, A., Eldin, A. S. 2021. Similarity-based machine learning framework for predicting safety signals of adverse drug–drug interactions. Informatics in Medicine Unlocked, 26, 100699.
  • [11] Yan, C., Duan, G., Zhang, Y., Wu, F. X., Pan, Y., Wang, J. 2020. Predicting drug-drug interactions based on integrated similarity and semi-supervised learning. IEEE/ACM Transactions on Computational Biology and Bioinformatics.
  • [12] Shi, J. Y., Huang, H., Li, J. X., Lei, P., Zhang, Y. N., Yiu, S. M. 2017, April. Predicting comprehensive drug-drug interactions for new drugs via triple matrix factorization. In International Conference on Bioinformatics and Biomedical Engineering, pp. 108-117. Springer, Cham.
  • [13] Zhang, W., Chen, Y., Li, D., Yue, X. 2018. Manifold regularized matrix factorization for drug-drug interaction prediction. Journal of biomedical informatics, 88, 90-97.
  • [14] Shi, J. Y., Huang, H., Li, J. X., Lei, P., Zhang, Y. N., Dong, K., Yiu, S. M. 2018. TMFUF: a triple matrix factorization-based unified framework for predicting comprehensive drug-drug interactions of new drugs. BMC bioinformatics, 19(14), 27-37.
  • [15] Shtar, G., Rokach, L., Shapira, B. 2019. Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures. PloS one, 14(8), e0219796.
  • [16] Rohani, N., Eslahchi, C., Katanforoush, A. 2020. Iscmf: Integrated similarity-constrained matrix factorization for drug–drug interaction prediction. Network Modeling Analysis in Health Informatics and Bioinformatics, 9(1), 1-8.
  • [17] Liu, S., Tang, B., Chen, Q., Wang, X. 2016. Drug-drug interaction extraction via convolutional neural networks. Computational and mathematical methods in medicine, 2016.
  • [18] Asada, M., Miwa, M., Sasaki, Y. 2017, August. Extracting drug-drug interactions with attention CNNs. In BioNLP 2017, pp. 9-18.
  • [19] Suárez-Paniagua, V., Segura-Bedmar, I. 2018. Evaluation of pooling operations in convolutional architectures for drug-drug interaction extraction. BMC bioinformatics, 19(8), 39-47.
  • [20] Wu, H., Xing, Y., Ge, W., Liu, X., Zou, J., Zhou, C., Liao, J. 2020. Drug-drug interaction extraction via hybrid neural networks on biomedical literature. Journal of biomedical informatics, 106, 103432.
  • [21] Deng, Y., Xu, X., Qiu, Y., Xia, J., Zhang, W., Liu, S. 2020. A multimodal deep learning framework for predicting drug–drug interaction events. Bioinformatics, 36(15), 4316-4322.
  • [22] Zhang, C., Lu, Y., Zang, T. 2022. CNN-DDI: a learning-based method for predicting drug–drug interactions using convolution neural networks. BMC bioinformatics, 23(1), 1-12.
  • [23] Feng, Y. H., Zhang, S. W., Zhang, Q. Q., Zhang, C. H., Shi, J. Y. 2022. deepMDDI: A deep graph convolutional network framework for multi-label prediction of drug-drug interactions. Analytical Biochemistry, 646, 114631.
  • [24] Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., ... & Wilson, M. 2018. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic acids research, 46(D1), D1074-D1082.
There are 24 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

Ramazan Özgür Doğan 0000-0001-6415-5755

Hülya Doğan 0000-0003-3695-8539

Feride Sena Sezen 0000-0002-7379-2518

Publication Date April 25, 2023
Published in Issue Year 2023 Volume: 27 Issue: 1

Cite

APA Doğan, R. Ö., Doğan, H., & Sezen, F. S. (2023). İlaç - İlaç Etkileşimi Tahmini için Konvolüsyonel Sinir Ağı Tabanlı Yeni Bir Yaklaşım. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 27(1), 137-144. https://doi.org/10.19113/sdufenbed.1182333
AMA Doğan RÖ, Doğan H, Sezen FS. İlaç - İlaç Etkileşimi Tahmini için Konvolüsyonel Sinir Ağı Tabanlı Yeni Bir Yaklaşım. SDÜ Fen Bil Enst Der. April 2023;27(1):137-144. doi:10.19113/sdufenbed.1182333
Chicago Doğan, Ramazan Özgür, Hülya Doğan, and Feride Sena Sezen. “İlaç - İlaç Etkileşimi Tahmini için Konvolüsyonel Sinir Ağı Tabanlı Yeni Bir Yaklaşım”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 27, no. 1 (April 2023): 137-44. https://doi.org/10.19113/sdufenbed.1182333.
EndNote Doğan RÖ, Doğan H, Sezen FS (April 1, 2023) İlaç - İlaç Etkileşimi Tahmini için Konvolüsyonel Sinir Ağı Tabanlı Yeni Bir Yaklaşım. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 27 1 137–144.
IEEE R. Ö. Doğan, H. Doğan, and F. S. Sezen, “İlaç - İlaç Etkileşimi Tahmini için Konvolüsyonel Sinir Ağı Tabanlı Yeni Bir Yaklaşım”, SDÜ Fen Bil Enst Der, vol. 27, no. 1, pp. 137–144, 2023, doi: 10.19113/sdufenbed.1182333.
ISNAD Doğan, Ramazan Özgür et al. “İlaç - İlaç Etkileşimi Tahmini için Konvolüsyonel Sinir Ağı Tabanlı Yeni Bir Yaklaşım”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 27/1 (April 2023), 137-144. https://doi.org/10.19113/sdufenbed.1182333.
JAMA Doğan RÖ, Doğan H, Sezen FS. İlaç - İlaç Etkileşimi Tahmini için Konvolüsyonel Sinir Ağı Tabanlı Yeni Bir Yaklaşım. SDÜ Fen Bil Enst Der. 2023;27:137–144.
MLA Doğan, Ramazan Özgür et al. “İlaç - İlaç Etkileşimi Tahmini için Konvolüsyonel Sinir Ağı Tabanlı Yeni Bir Yaklaşım”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 27, no. 1, 2023, pp. 137-44, doi:10.19113/sdufenbed.1182333.
Vancouver Doğan RÖ, Doğan H, Sezen FS. İlaç - İlaç Etkileşimi Tahmini için Konvolüsyonel Sinir Ağı Tabanlı Yeni Bir Yaklaşım. SDÜ Fen Bil Enst Der. 2023;27(1):137-44.
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