Improving Drug-Drug Interaction Extraction with Gaussian Noise

Overview

Journal Pharmaceutics

Publisher MDPI

Date 2023 Jul 29

PMID 37514010

Authors

Marco Molina

Cristina Jimenez

Carlos Montenegro

Affiliations

Soon will be listed here.

Abstract

Drug-Drug Interactions (DDIs) produce essential and valuable insights for healthcare professionals, since they provide data on the impact of concurrent administration of medications to patients during therapy. In that sense, some relevant works, related to the DDIExtraction2013 Challenge, are available in the current technical literature. This study aims to improve previous results, using two models, where a Gaussian noise layer is added to achieve better DDI relationship extraction. (1) A Piecewise Convolutional Neural Network (PW-CNN) model is used to capture relationships among pharmacological entities described in biomedical databases. Additionally, the model incorporates multichannel words to enrich a person's vocabulary and reduce unfamiliar words. (2) The model uses the pre-trained BERT language model to classify relationships, while also integrating data from the target entities. After identifying the target entities, the model transfers the relevant information through the pre-trained architecture and integrates the encoded data for both entities. The results of the experiment show an improved performance, with respect to previous models.

Citing Articles

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References

Yang Z, Lin H, Li Y . BioPPISVMExtractor: a protein-protein interaction extractor for biomedical literature using SVM and rich feature sets. J Biomed Inform. 2009; 43(1):88-96. DOI: 10.1016/j.jbi.2009.08.013. View

Quan C, Hua L, Sun X, Bai W . Multichannel Convolutional Neural Network for Biological Relation Extraction. Biomed Res Int. 2017; 2016:1850404. PMC: 5174749. DOI: 10.1155/2016/1850404. View

Huang L, Lin J, Li X, Song L, Zheng Z, Wong K . EGFI: drug-drug interaction extraction and generation with fusion of enriched entity and sentence information. Brief Bioinform. 2021; 23(1). DOI: 10.1093/bib/bbab451. View

Herrero-Zazo M, Segura-Bedmar I, Martinez P, Declerck T . The DDI corpus: an annotated corpus with pharmacological substances and drug-drug interactions. J Biomed Inform. 2013; 46(5):914-20. DOI: 10.1016/j.jbi.2013.07.011. View

Liu S, Tang B, Chen Q, Wang X . Drug-Drug Interaction Extraction via Convolutional Neural Networks. Comput Math Methods Med. 2016; 2016:6918381. PMC: 4752975. DOI: 10.1155/2016/6918381. View

Miranda V, Fede A, Nobuo M, Ayres V, Giglio A, Miranda M . Adverse drug reactions and drug interactions as causes of hospital admission in oncology. J Pain Symptom Manage. 2011; 42(3):342-53. DOI: 10.1016/j.jpainsymman.2010.11.014. View

Shorten C, Khoshgoftaar T, Furht B . Text Data Augmentation for Deep Learning. J Big Data. 2021; 8(1):101. PMC: 8287113. DOI: 10.1186/s40537-021-00492-0. View

Lamurias A, Sousa D, Clarke L, Couto F . BO-LSTM: classifying relations via long short-term memory networks along biomedical ontologies. BMC Bioinformatics. 2019; 20(1):10. PMC: 6323831. DOI: 10.1186/s12859-018-2584-5. View

Zhu Y, Li L, Lu H, Zhou A, Qin X . Extracting drug-drug interactions from texts with BioBERT and multiple entity-aware attentions. J Biomed Inform. 2020; 106:103451. DOI: 10.1016/j.jbi.2020.103451. View

10.

Zhao Z, Yang Z, Luo L, Lin H, Wang J . Drug drug interaction extraction from biomedical literature using syntax convolutional neural network. Bioinformatics. 2016; 32(22):3444-3453. PMC: 5181565. DOI: 10.1093/bioinformatics/btw486. View

11.

Kim S, Liu H, Yeganova L, Wilbur W . Extracting drug-drug interactions from literature using a rich feature-based linear kernel approach. J Biomed Inform. 2015; 55:23-30. PMC: 4464931. DOI: 10.1016/j.jbi.2015.03.002. View

12.

Pyysalo S, Airola A, Heimonen J, Bjorne J, Ginter F, Salakoski T . Comparative analysis of five protein-protein interaction corpora. BMC Bioinformatics. 2008; 9 Suppl 3:S6. PMC: 2349296. DOI: 10.1186/1471-2105-9-S3-S6. View

13.

Duda S, Aliferis C, Miller R, Statnikov A, Johnson K . Extracting drug-drug interaction articles from MEDLINE to improve the content of drug databases. AMIA Annu Symp Proc. 2006; :216-20. PMC: 1560879. View

14.

Bui Q, Sloot P, van Mulligen E, Kors J . A novel feature-based approach to extract drug-drug interactions from biomedical text. Bioinformatics. 2014; 30(23):3365-71. DOI: 10.1093/bioinformatics/btu557. View

15.

Zhou D, Miao L, He Y . Position-aware deep multi-task learning for drug-drug interaction extraction. Artif Intell Med. 2018; 87:1-8. DOI: 10.1016/j.artmed.2018.03.001. View

16.

Zhang Y, Zheng W, Lin H, Wang J, Yang Z, Dumontier M . Drug-drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths. Bioinformatics. 2017; 34(5):828-835. PMC: 6030919. DOI: 10.1093/bioinformatics/btx659. View