Using deep learning to identify translational research in genomic medicine beyond bench to bedside
Open accessAbstract
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.
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Several computational methods have been proposed as an effective alternative to keyword-based query methods for literature recommendation.
“Several computational methods have been proposed as an effective alternative to keyword-based query methods for literature recommendation.”
From LitSuggest: a web-based system for literature recommendation and curation using machine learning (2021) · cites this paper as [14]
The Introduction and Utility assessment (pages not specified) describe computational methods for classifying and prioritizing relevant articles. They do not establish these methods as alternatives to keyword-based queries: in the assessed workflow, PubMed queries still retrieve articles, and the classifier ranks the results for review. The paper also concerns genomic-research curation rather than literature recommendation generally. (LLM, conf=0.86)
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- Authors
- Hsu, Y.-Y., Clyne, M., Wei, C.-H., Khoury, M. J., & Lu, Z.
- Affiliations
- National Institutes of Health (US)
- National Cancer Institute (US)
- Centers for Disease Control and Prevention (US)
- Corresponding author
- Khoury, Muin J
- Lu, Zhiyong
- Venue
- Database
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-01-01
- Volume
- 2019
- ISSN
- 1758-0463 (ISSN-L)
- PMC
- PMC6367517
- OpenAlex
- W2914293296
- Cited by
- 6
- References
- 23
- Citations by year
- 2026: 2 · 2023: 1 · 2022: 1 · 2021: 2
- FWCI
- 0.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- public-domain
- Locations
- Database (publishedVersion, public-domain)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Intramural Research Programs of the National Institutes of Health, National Library of Medicine
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- translational research, genomic medicine, bench-to-bedside, public health genomics, convolutional neural networks, support vector machine, annotation workload reduction
- MeSH terms
- Deep Learning, ROC Curve, Statistics as Topic, Task Performance and Analysis, Reproducibility of Results, Neural Networks, Computer, Genomics, Translational Research, Biomedical, Support Vector Machine
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