Cargando…
Using Nanoinformatics Methods for Automatically Identifying Relevant Nanotoxicology Entities from the Literature
Nanoinformatics is an emerging research field that uses informatics techniques to collect, process, store, and retrieve data, information, and knowledge on nanoparticles, nanomaterials, and nanodevices and their potential applications in health care. In this paper, we have focused on the solutions t...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3591181/ https://www.ncbi.nlm.nih.gov/pubmed/23509721 http://dx.doi.org/10.1155/2013/410294 |
_version_ | 1782261998284701696 |
---|---|
author | García-Remesal, Miguel García-Ruiz, Alejandro Pérez-Rey, David de la Iglesia, Diana Maojo, Víctor |
author_facet | García-Remesal, Miguel García-Ruiz, Alejandro Pérez-Rey, David de la Iglesia, Diana Maojo, Víctor |
author_sort | García-Remesal, Miguel |
collection | PubMed |
description | Nanoinformatics is an emerging research field that uses informatics techniques to collect, process, store, and retrieve data, information, and knowledge on nanoparticles, nanomaterials, and nanodevices and their potential applications in health care. In this paper, we have focused on the solutions that nanoinformatics can provide to facilitate nanotoxicology research. For this, we have taken a computational approach to automatically recognize and extract nanotoxicology-related entities from the scientific literature. The desired entities belong to four different categories: nanoparticles, routes of exposure, toxic effects, and targets. The entity recognizer was trained using a corpus that we specifically created for this purpose and was validated by two nanomedicine/nanotoxicology experts. We evaluated the performance of our entity recognizer using 10-fold cross-validation. The precisions range from 87.6% (targets) to 93.0% (routes of exposure), while recall values range from 82.6% (routes of exposure) to 87.4% (toxic effects). These results prove the feasibility of using computational approaches to reliably perform different named entity recognition (NER)-dependent tasks, such as for instance augmented reading or semantic searches. This research is a “proof of concept” that can be expanded to stimulate further developments that could assist researchers in managing data, information, and knowledge at the nanolevel, thus accelerating research in nanomedicine. |
format | Online Article Text |
id | pubmed-3591181 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-35911812013-03-18 Using Nanoinformatics Methods for Automatically Identifying Relevant Nanotoxicology Entities from the Literature García-Remesal, Miguel García-Ruiz, Alejandro Pérez-Rey, David de la Iglesia, Diana Maojo, Víctor Biomed Res Int Research Article Nanoinformatics is an emerging research field that uses informatics techniques to collect, process, store, and retrieve data, information, and knowledge on nanoparticles, nanomaterials, and nanodevices and their potential applications in health care. In this paper, we have focused on the solutions that nanoinformatics can provide to facilitate nanotoxicology research. For this, we have taken a computational approach to automatically recognize and extract nanotoxicology-related entities from the scientific literature. The desired entities belong to four different categories: nanoparticles, routes of exposure, toxic effects, and targets. The entity recognizer was trained using a corpus that we specifically created for this purpose and was validated by two nanomedicine/nanotoxicology experts. We evaluated the performance of our entity recognizer using 10-fold cross-validation. The precisions range from 87.6% (targets) to 93.0% (routes of exposure), while recall values range from 82.6% (routes of exposure) to 87.4% (toxic effects). These results prove the feasibility of using computational approaches to reliably perform different named entity recognition (NER)-dependent tasks, such as for instance augmented reading or semantic searches. This research is a “proof of concept” that can be expanded to stimulate further developments that could assist researchers in managing data, information, and knowledge at the nanolevel, thus accelerating research in nanomedicine. Hindawi Publishing Corporation 2013 2012-12-27 /pmc/articles/PMC3591181/ /pubmed/23509721 http://dx.doi.org/10.1155/2013/410294 Text en Copyright © 2013 Miguel García-Remesal et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article García-Remesal, Miguel García-Ruiz, Alejandro Pérez-Rey, David de la Iglesia, Diana Maojo, Víctor Using Nanoinformatics Methods for Automatically Identifying Relevant Nanotoxicology Entities from the Literature |
title | Using Nanoinformatics Methods for Automatically Identifying Relevant Nanotoxicology Entities from the Literature |
title_full | Using Nanoinformatics Methods for Automatically Identifying Relevant Nanotoxicology Entities from the Literature |
title_fullStr | Using Nanoinformatics Methods for Automatically Identifying Relevant Nanotoxicology Entities from the Literature |
title_full_unstemmed | Using Nanoinformatics Methods for Automatically Identifying Relevant Nanotoxicology Entities from the Literature |
title_short | Using Nanoinformatics Methods for Automatically Identifying Relevant Nanotoxicology Entities from the Literature |
title_sort | using nanoinformatics methods for automatically identifying relevant nanotoxicology entities from the literature |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3591181/ https://www.ncbi.nlm.nih.gov/pubmed/23509721 http://dx.doi.org/10.1155/2013/410294 |
work_keys_str_mv | AT garciaremesalmiguel usingnanoinformaticsmethodsforautomaticallyidentifyingrelevantnanotoxicologyentitiesfromtheliterature AT garciaruizalejandro usingnanoinformaticsmethodsforautomaticallyidentifyingrelevantnanotoxicologyentitiesfromtheliterature AT perezreydavid usingnanoinformaticsmethodsforautomaticallyidentifyingrelevantnanotoxicologyentitiesfromtheliterature AT delaiglesiadiana usingnanoinformaticsmethodsforautomaticallyidentifyingrelevantnanotoxicologyentitiesfromtheliterature AT maojovictor usingnanoinformaticsmethodsforautomaticallyidentifyingrelevantnanotoxicologyentitiesfromtheliterature |