Using deep learning to identify translational research in genomic medicine beyond bench to bedside

被引:3
|
作者
Hsu, Yi-Yu [1 ]
Clyne, Mindy [2 ]
Wei, Chih-Hsuan [1 ]
Khoury, Muin J. [3 ]
Lu, Zhiyong [1 ]
机构
[1] Natl Lib Med, Natl Ctr Biotechnol Informat, Bethesda, MD 20894 USA
[2] NCI, Implementat Sci Team, Div Canc Control & Populat Sci, Bethesda, MD 20892 USA
[3] Ctr Dis Control & Prevent, Off Publ Hlth Genom, Atlanta, GA 30333 USA
来源
DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION | 2019年
基金
美国国家卫生研究院;
关键词
D O I
10.1093/database/baz010
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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 vectormachines (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.
引用
收藏
页数:6
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