Abstract
Drug repurposing, which is concerned with the study of the effectiveness of existing drugs on new diseases, has been growing in importance in the last few years. One of the core methodologies for drug repurposing is text-mining, where novel biological entity relationships are extracted from existing biomedical literature and publications, whose number skyrocketed in the last couple of years. This paper proposes an NLP approach for drug-disease relation discovery and labeling (DD-RDL), which employs a series of steps to analyze a corpus of abstracts of scientific biomedical research papers. The proposed ML pipeline restructures the free text from a set of words into drug-disease pairs using state-of-the-art text mining methodologies and natural language processing tools. The model’s output is a set of extracted triplets in the form (drug, verb, disease), where each triple describes a relationship between a drug and a disease detected in the corpus. We evaluate the model based on a gold standard dataset for drug-disease relationships, and we demonstrate that it is possible to achieve similar results without requiring a large amount of annotated biological data or predefined semantic rules. Additionally, as an experimental case, we analyze the research papers published as part of the COVID-19 Open Research Dataset (CORD-19) to extract and identify relations between drugs and diseases related to the ongoing pandemic.
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References
Abacha, A.B., Zweigenbaum, P.: Automatic extraction of semantic relations between medical entities: a rule based approach. J. Biomed. Seman. 2(5), 1–11 (2011)
Aho, A.V., Corasick, M.J.: Efficient string matching: an aid to bibliographic search. Commun. ACM 18(6), 333–340 (1975)
Dobreva, J., Jofche, N., Jovanovik, M., Trajanov, D.: Improving NER performance by applying text summarization on pharmaceutical articles. In: Dimitrova, V., Dimitrovski, I. (eds.) ICT Innovations 2020. CCIS, vol. 1316, pp. 87–97. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62098-1_8
Filannino, M., Uzuner, Ö.: Advancing the state of the art in clinical natural language processing through shared tasks. Yearbook Med. Inform. 27(1), 184 (2018)
Fu, T.y., Lee, W.C., Lei, Z.: Hin2vec: explore meta-paths in heterogeneous information networks for representation learning. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1797–1806 (2017)
Gardner, M., et al.: AllenNLP: a deep semantic natural language processing platform. In: Proceedings of Workshop for NLP Open Source Software (NLP-OSS) (2018)
Gottlieb, A., Stein, G.Y., Ruppin, E., Sharan, R.: Predict: a method for inferring novel drug indications with application to personalized medicine. Mole. Syst. Biol. 7(1), 496 (2011)
Gu, J., Qian, L., Zhou, G.: Chemical-induced disease relation extraction with various linguistic features. Database 2016, 042 (2016)
Henry, S., McInnes, B.T.: Literature based discovery: models, methods, and trends. J. Biomed. Inform. 74, 20–32 (2017).https://doi.org/10.1016/j.jbi.2017.08.011,https://www.sciencedirect.com/science/article/pii/S1532046417301909
Jofche, N., Mishev, K., Stojanov, R., Jovanovik, M., Trajanov, D.: PharmKE: Knowledge extraction platform for pharmaceutical texts using transfer learning (2021)
Kadir, R.A., Bokharaeian, B.: Overview of biomedical relations extraction using hybrid rulebased approaches. J. Ind. Intell. Inf. 1(3) (2013)
Khan, J.Y., et al.: COVID-19Base: a knowledgebase to explore biomedical entities related to COVID-19. arXiv preprint arXiv:2005.05954 (2020)
Kilicoglu, H., Rosemblat, G., Fiszman, M., Shin, D.: Broad-coverage biomedical relation extraction with Semrep. BMC Bioinform. 21, 1–28 (2020)
Kraljevic, Z., et al.: MedCAT - Medical Concept Annotation Tool (2019)
Lee, K., He, L., Lewis, M., Zettlemoyer, L.: End-to-end neural co reference resolution. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 188–197. Association for Computational Linguistics, Copenhagen, Denmark, September 2017. https://doi.org/10.18653/v1/D17-1018, https://www.aclweb.org/anthology/D17-1018
Li, J., Sun, A., Han, J., Li, C.: A survey on deep learning for named entity recognition. CoRR abs/1812.09449 (2018), http://arxiv.org/abs/1812.09449
Liu, J., Abeysinghe, R., Zheng, F., Cui, L.: Pattern-based extraction of disease drug combination knowledge from biomedical literature. In: 2019 IEEE International Conference on Healthcare Informatics (ICHI), pp. 1–7. IEEE (2019)
Màrquez, L., Carreras, X., Litkowski, K.C., Stevenson, S.: Semantic role labeling: an introduction to the special issue. Comput. Ling. 34, 145–159 (2008)
Preiss, J., Stevenson, M., Gaizauskas, R.: Exploring relation types for literature-based discovery. J. Am. Med. Inform. Assoc 22(5), 987–992 (2015). https://doi.org/10.1093/jamia/ocv002
Qi, P., Zhang, Y., Zhang, Y., Bolton, J., Manning, C.D.: Stanza: a python natural language processing toolkit for many human languages. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 101–108 (2020)
Rosario, B., Hearst, M.A.: Classifying semantic relations in bioscience texts. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-2004), pp. 430–437 (2004)
Sang, S., Yang, Z., Wang, L., Liu, X., Lin, H., Wang, J.: SemaTyP: a knowledge graph based literature mining method for drug discovery. BMC Bioinform. 19(1), 1–11 (2018)
Shi, P., Lin, J.: Simple BERT models for relation extraction and semantic role labeling (2019)
Soon, W.M., Ng, H.T., Lim, D.C.Y.: A machine learning approach to coreference resolution of noun phrases. Comput. Linguist. 27(4), 521–544 (2001). https://doi.org/10.1162/089120101753342653, https://www.aclweb.org/anthology/J01-4004
Torfi, A., Shirvani, R.A., Keneshloo, Y., Tavaf, N., Fox, E.A.: Natural Language Processing Advancements By Deep Learning: A Survey (2020)
Wang, L.L., et al.: CORD-19: The COVID-19 open research dataset (2020)
Wang, P., Hao, T., Yan, J., Jin, L.: Large-scale extraction of drug-disease pairs from the medical literature. J. Assoc. Inf. Sci. Technol. 68(11), 2649–2661 (2017)
Wolf, T., et al.: HuggingFace’s transformers: state-of-the-art natural language processing. In: Proceedings of the 2020 EMNLP (Systems Demonstrations), pp. 38–45 (2020)
Xia, Q., et al.: Syntax-aware neural semantic role labeling (2019)
Xu, R., Wang, Q.: Large-scale extraction of accurate drug-disease treatment pairs from biomedical literature for drug repurposing. BMC Bioinform. 14(1), 1–11 (2013)
Xue, H., Li, J., Xie, H., Wang, Y.: Review of drug repositioning approaches and resources. Int. J. Biol. Sci.Int. J. Biol. Sci. 14(10), 1232 (2018)
Yang, H., Swaminathan, R., Sharma, A., Ketkar, V., Jason, D.: Mining biomedical text towards building a quantitative food-disease-gene network. In: Learning Structure and Schemas from Documents, pp. 205–225. Springer, Cham (2011). https://doi.org/10.1007/978-3-642-22913-8
Zhou, R., Lu, Z., Luo, H., Xiang, J., Zeng, M., Li, M.: NEDD: a network embedding based method for predicting drug-disease associations. BMC Bioinform. 21(13), 1–12 (2020)
Acknowledgement
The work in this paper was partially financed by the Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje.
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Dobreva, J., Jovanovik, M., Trajanov, D. (2022). DD-RDL: Drug-Disease Relation Discovery and Labeling. In: Antovski, L., Armenski, G. (eds) ICT Innovations 2021. Digital Transformation. ICT Innovations 2021. Communications in Computer and Information Science, vol 1521. Springer, Cham. https://doi.org/10.1007/978-3-031-04206-5_8
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