Scaling up data curation using deep learning: An application to literature triage in genomic variation resources.

Détails

Ressource 1Télécharger: journal.pcbi.1006390.pdf (791.54 [Ko])
Etat: Public
Version: Final published version
ID Serval
serval:BIB_916AEEE29E4F
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Scaling up data curation using deep learning: An application to literature triage in genomic variation resources.
Périodique
PLoS computational biology
Auteur(s)
Lee K., Famiglietti M.L., McMahon A., Wei C.H., MacArthur JAL, Poux S., Breuza L., Bridge A., Cunningham F., Xenarios I., Lu Z.
ISSN
1553-7358 (Electronic)
ISSN-L
1553-734X
Statut éditorial
Publié
Date de publication
08/2018
Peer-reviewed
Oui
Volume
14
Numéro
8
Pages
e1006390
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural ; Research Support, N.I.H., Intramural ; Research Support, Non-U.S. Gov't
Publication Status: epublish
Résumé
Manually curating biomedical knowledge from publications is necessary to build a knowledge based service that provides highly precise and organized information to users. The process of retrieving relevant publications for curation, which is also known as document triage, is usually carried out by querying and reading articles in PubMed. However, this query-based method often obtains unsatisfactory precision and recall on the retrieved results, and it is difficult to manually generate optimal queries. To address this, we propose a machine-learning assisted triage method. We collect previously curated publications from two databases UniProtKB/Swiss-Prot and the NHGRI-EBI GWAS Catalog, and used them as a gold-standard dataset for training deep learning models based on convolutional neural networks. We then use the trained models to classify and rank new publications for curation. For evaluation, we apply our method to the real-world manual curation process of UniProtKB/Swiss-Prot and the GWAS Catalog. We demonstrate that our machine-assisted triage method outperforms the current query-based triage methods, improves efficiency, and enriches curated content. Our method achieves a precision 1.81 and 2.99 times higher than that obtained by the current query-based triage methods of UniProtKB/Swiss-Prot and the GWAS Catalog, respectively, without compromising recall. In fact, our method retrieves many additional relevant publications that the query-based method of UniProtKB/Swiss-Prot could not find. As these results show, our machine learning-based method can make the triage process more efficient and is being implemented in production so that human curators can focus on more challenging tasks to improve the quality of knowledge bases.
Mots-clé
Data Curation/methods, Data Curation/statistics & numerical data, Databases, Genetic, Databases, Protein, Deep Learning, Genomics, Information Storage and Retrieval/methods, Knowledge Bases, Machine Learning, Publications
Pubmed
Web of science
Open Access
Oui
Création de la notice
20/08/2018 12:53
Dernière modification de la notice
16/09/2019 5:26
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