27.09.2024; Суми, Україна: VIII Міжнародна наукова конференція «Наукові тренди постіндустріального суспільства»
Роботи, що індексуються в Google Scholar

PREDICTION OF DRUG-DRUG INTERACTIONS BY SUBSTANCE STRUCTURAL SIMILARITY WITH HELP OF NEURAL NETWORKS

  • Roman Vovk
DOI: https://doi.org/10.62731/mcnd-27.09.2024.002
PDF

Опубліковано 14.10.2024

Як цитувати

Vovk , R. (2024). PREDICTION OF DRUG-DRUG INTERACTIONS BY SUBSTANCE STRUCTURAL SIMILARITY WITH HELP OF NEURAL NETWORKS. Матеріали конференцій МЦНД, (27.09.2024; Суми, Україна), 153–161. https://doi.org/10.62731/mcnd-27.09.2024.002

Завантаження

Дані завантаження ще не доступні.

Авторське право (c) 2024 Roman Vovk

Creative Commons License

Ця робота ліцензується відповідно до Creative Commons Attribution-ShareAlike 4.0 International License.

Google Scholar

Анотація

Prediction of drug-drug interactions (DDI) is a new area of research in the field of pharmaceuticals. The main goal of these studies is to minimize risks for the patient caused by certain adverse drug reactions (hereinafter ADR) and to prevent cases of the patient taking combinations of drugs that excessively weaken or enhance the effect of each other on the human body. Effective prediction of any type of DDI is a way to reduce the intensity and frequency of side effects, and improve the effectiveness of therapy.

PDF

Посилання

  1. 1. He C. Multi-type feature fusion based on graph neural network for drug-drug interaction prediction / C. He, Y. Liu, H. Li, [et al.] // BMC Bioinformatics. — 2022. — Vol. 23, No. 1.
  2. 2. Schwarz K. AttentionDDI: siamese attention-based deep learning method for drug–drug interaction predictions / K. Schwarz, A. Allam, N. A. Perez Gonzalez, M. Krauthammer // BMC Bioinformatics. — 2021. — Vol. 22, No. 1.
  3. 3. Luo Q. Novel deep learning-based transcriptome data analysis for drug-drug interaction prediction with an application in diabetes / Q. Luo, S. Mo, Y. Xue, [et al.] // BMC Bioinformatics. — 2021. — Vol. 22, No. 1.
  4. 4. Bumgardner B. Drug-drug interaction prediction: a purely smiles based approach /
  5. B. Bumgardner, F. Tanvir, K. M. Saifuddin, E. Akbas. — 2021.
  6. 5. Feng Y.-H. DPDDI: a deep predictor for drug-drug interactions / Y.-H. Feng, S.-W. Zhang, J.-Y. Shi // BMC Bioinformatics. — 2020. — Vol. 21, No. 1. — P. 419.
  7. 6. Lin S. DeepPSE: prediction of polypharmacy side effects by fusing deep representation of drug pairs and attention mechanism / S. Lin, G. Zhang, D.-Q. Wei, Y. Xiong // Computers in Biology and Medicine. — 2022. — Vol. 149.
  8. 7. Ziaee S. S. Dcomg: drug combination prediction by applying gnns on ddi node2vec features /
  9. S. S. Ziaee, H. Rahmani, M. Tabatabaei. — 2022.
  10. 8. Liu T. Modeling polypharmacy effects with heterogeneous signed graph convolutional networks / T. Liu, J. Cui, H. Zhuang, H. Wang // Applied Intelligence. — 2021. — Vol. 51, No. 11. — P. 8316–8333.
  11. 9. Shi D. A mortality risk assessment approach on icu patients clinical medication events using deep learning / D. Shi, H. Zheng // CMES - Computer Modeling in Engineering and Sciences. — 2021. — Vol. 128, No. 1. — P. 161–181.
  12. 10. Wang M. Predicting rich drug-drug interactions via biomedical knowledge graphs and text jointly embedding / M. Wang. — arXiv, 2018.
  13. 11. Kim E. DeSIDE-ddi: interpretable prediction of drug-drug interactions using drug-induced gene expressions / E. Kim, H. Nam // Journal of Cheminformatics. — 2022. — Vol. 14, No. 1. — P. 9.
  14. 12. DrugBank online | database for drug and drug target info [електронний ресурс]. режим доступу: https://go.drugbank.com/ / .
  15. 13. Quirós M. Using smiles strings for the description of chemical connectivity in the crystallography open database / M. Quirós, S. Gražulis, S. Girdzijauskaitė, [et al.] // Journal of Cheminformatics. — 2018. — Vol. 10, No. 1. — P. 23.
  16. 14. Resource description framework (rdf) model and syntax specification [електронний ресурс]. режим доступу: https://www.w3.org/tr/pr-rdf-syntax/overview.html / .
  17. 15. Ristoski P. RDF2Vec: rdf graph embeddings and their applications / P. Ristoski, J. Rosati,
  18. T. Di Noia, [et al.] // Semantic Web. — 2019. — Vol. 10, No. 4. — P. 721–752.
  19. 16. Belleau F. Bio2RDF: towards a mashup to build bioinformatics knowledge systems / F. Belleau, M.-A. Nolin, N. Tourigny, [et al.] // Journal of Biomedical Informatics. — 2008. — Vol. 41, No. 5. — P. 706–716.
  20. 17. Mikolov T. Efficient estimation of word representations in vector space / T. Mikolov, K. Chen,
  21. G. Corrado, J. Dean. — arXiv, 2013.
  22. 18. O’Shea K. An introduction to convolutional neural networks / K. O’Shea, R. Nash. — arXiv, 2015.
  23. 19. Friedman J. H. Greedy function approximation: a gradient boosting machine. / J. H. Friedman // The Annals of Statistics. — 2001. — Vol. 29, No. 5. — P. 1189–1232.
  24. 20. Ke G. LightGBM: a highly efficient gradient boosting decision tree / G. Ke, Q. Meng, T. Finley, [et al.]. — Curran Associates, Inc., 2017.