Using deep learning to identify translational research in genomic medicine beyond bench to bedside.
Database (Oxford)
; 20192019 01 01.
Article
em En
| MEDLINE
| ID: mdl-30753477
ABSTRACT
Tracking scientific research publications on the evaluation, utility and implementation of genomic applications is critical for the translation of basic research to impact clinical and population health. In this work, we utilize state-of-the-art machine learning approaches to identify translational research in genomics beyond bench to bedside from the biomedical literature. We apply the convolutional neural networks (CNNs) and support vector machines (SVMs) to the bench/bedside article classification on the weekly manual annotation data of the Public Health Genomics Knowledge Base database. Both classifiers employ salient features to determine the probability of curation-eligible publications, which can effectively reduce the workload of manual triage and curation process. We applied the CNNs and SVMs to an independent test set (n = 400), and the models achieved the F-measure of 0.80 and 0.74, respectively. We further tested the CNNs, which perform better results, on the routine annotation pipeline for 2 weeks and significantly reduced the effort and retrieved more appropriate research articles. Our approaches provide direct insight into the automated curation of genomic translational research beyond bench to bedside. The machine learning classifiers are found to be helpful for annotators to enhance the efficiency of manual curation.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Genômica
/
Pesquisa Translacional Biomédica
/
Aprendizado Profundo
Tipo de estudo:
Guideline
/
Prognostic_studies
Idioma:
En
Revista:
Database (Oxford)
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Estados Unidos