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 Journal of Statistics and Management Systems cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510

Monthly Journal: Publishes peer-reviewed aticles on theoretical and applied statistics and management systems, expoloring industrial statistics, actuarial and decision sciences.

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Open Access Research Article

Natural language processing for drug information extraction : Advancing knowledge discovery in biomedical literature

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pp. 383–393Vol. 27Issue 2March 2024DOI: 10.47974/JSMS-1263XML
Published Online:
30 Mar 2024
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1263
Pages:
383–393

Abstract

Natural language processing (NLP) has emerged as an important tool in the biomedical industry for bettering medication information extraction and other knowledge gathering. In this study, we explore the use of natural language processing (NLP) techniques to glean useful insights from massive volumes of biological literature, with the goal of better comprehending data pertaining to drugs. We want to automate the extraction of crucial aspects such drug interactions, side effects, and efficacy by utilizing sophisticated language models and semantic analysis. Effective and comprehensive drug information retrieval will be encouraged. Our innovation improves the drug development process by facilitating the rapid exploration of big databases for previously unknown connections and patterns. By facilitating the synthesis of data from several sources, NLP in biomedical research speeds up information extraction, which in turn accelerates drug development and improves patient care by allowing for evidence-based decision making. This research demonstrates the promising potential of natural language processing (NLP) for unraveling the complexities of drug-related data, ushering in a new era of learning in the biomedical field. 

Keywords

Subject Classifications

68N15

References

[1] Y. Zhang and Z. Lu, “Exploring semi-supervised variational autoencoders for biomedical relation extraction,” Methods, vol. 166, pp. 112–119 (2019).
[2] Z. Li, Z. Yang, C. Shen, J. Xu, Y. Zhang, and H. Xu, “Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text,” BMC Medical Informatics and Decision Making, vol. 19, no. 1, p. 22 (2019).
[3] Y. Niu, D. Otasek, and I. Jurisica, “Evaluation of linguistic features useful in extraction of interactions from PubMed; application to annotating known, high-throughput and predicted interactions in I2D,” Bioinformatics, vol. 26, no. 1, pp. 111–119 (2009).
[4] Y. Zhu, L. Li, H. Lu, A. Zhou, and X. Qin, “Extracting drug-drug interactions from texts with BioBERT and multiple entity-aware attentions,” Journal of Biomedical Informatics, vol. 106, Article ID 103451 (2020).
[5] T. T. T. Phan, T. Ohkawa, and A. Yamamoto, “Protein-protein interaction extraction from text by selecting linguistic features,” in Proceedings of the 2017 IEEE 17th International Conference on Bioinformatics And Bioengineering (BIBE), pp. 181–187, Washington, DC, USA, October (2017).
[6] Y. Niu, H. Wu, and Y. Wang, “Protein-protein interaction identification using a similarity-constrained graph model,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 16, no. 2, pp. 607–616 (2019).
[7] D. Badal Varsha, J. Petras Kundrotas, and A. Ilya Vakser, “Natural language processing in text mining for structural modeling of protein complexes,” BMC Bioinformatics, vol. 19, no. 1 (2018).
[8] S. Koyabu, T. T. T. Phan, and T. Ohkawa, “Extraction of protein-protein interaction from scientific articles by predicting dominant keywords,” BioMed research international, vol. 2015, Article ID 928531, 13 pages (2015).
[9] D. Couch, Z. Yu, J. H. Nam et al., “GAIL: An interactive webserver for inference and dynamic visualization of gene-gene associations based on gene ontology guided mining of biomedical literature,” PLoS One, vol. 14, no. 7 (2019).
[10] Al-Aamri, K. Taha, Y. Al-Hammadi, M. Maalouf, and D. Homouz, “Analyzing a co-occurrence gene-interaction network to identify disease-gene association,” BMC Bioinformatics, vol. 20, no. 1, p. 70, (2019).

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