Exploring convolutional neural networks for drug-drug interaction extraction

e-Archivo Repository

Show simple item record

dc.contributor.author Suárez Paniagua, Víctor
dc.contributor.author Segura-Bedmar, Isabel
dc.contributor.author Martínez Fernández, Paloma
dc.date.accessioned 2020-10-30T10:06:26Z
dc.date.available 2020-10-30T10:06:26Z
dc.date.issued 2017-05-25
dc.identifier.bibliographicCitation Suárez-Paniagua,V., Segura-Bedmar,I. and Martínez,P. Exploring convolutional neural network for drug–drug interaction extraction. Database (2017) Vol. 2017: article ID bax019
dc.identifier.issn 1758-0463
dc.identifier.uri http://hdl.handle.net/10016/31325
dc.description.abstract Drug-drug interaction (DDI), which is a specific type of adverse drug reaction, occurs when a drug influences the level or activity of another drug. Natural language processing techniques can provide health-care professionals with a novel way of reducing the time spent reviewing the literature for potential DDIs. The current state-of-the-art for the extraction of DDIs is based on feature-engineering algorithms (such as support vector machines), which usually require considerable time and effort. One possible alternative to these approaches includes deep learning. This technique aims to automatically learn the best feature representation from the input data for a given task. The purpose of this paper is to examine whether a convolutional neural network (CNN), which only uses word embeddings as input features, can be applied successfully to classify DDIs from biomedical texts. Proposed herein, is a CNN architecture with only one hidden layer, thus making the model more computationally efficient, and we perform detailed experiments in order to determine the best settings of the model. The goal is to determine the best parameter of this basic CNN that should be considered for future research. The experimental results show that the proposed approach is promising because it attained the second position in the 2013 rankings of the DDI extraction challenge. However, it obtained worse results than previous works using neural networks with more complex architectures.
dc.description.sponsorship Research Program of the Ministry of Economy, Industry and Competitiveness, Government of Spain [eGovernAbility-Access project TIN2014-52665-C2-2-R].
dc.language.iso eng
dc.publisher Oxford University Press
dc.rights © The Author(s) 2017. Published by Oxford University Press.
dc.rights Atribución 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by/3.0/es/
dc.title Exploring convolutional neural networks for drug-drug interaction extraction
dc.type article
dc.subject.eciencia Informática
dc.identifier.doi https://doi.org/10.1093/database/bax019
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TIN2014-52665-C2-2-R
dc.type.version publishedVersion
dc.identifier.publicationtitle Database-The Journal of Biological Databases and Curation
dc.identifier.uxxi AR/0000020125
dc.contributor.funder Ministerio de Economía y Competitividad (España)
 Find Full text

Files in this item

*Click on file's image for preview. (Embargoed files's preview is not supported)

The following license files are associated with this item:

This item appears in the following Collection(s)

Show simple item record