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Scaling up data curation using deep learning: An application to literature triage in genomic variation resources
- Source :
- PLoS Computational Biology, Vol 14, Iss 8, p e1006390 (2018), PLoS computational biology, vol. 14, no. 8, pp. e1006390, PLoS Computational Biology
- Publication Year :
- 2018
- Publisher :
- Public Library of Science (PLoS), 2018.
-
Abstract
- 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.<br />Author summary As the volume of literature on genomic variants continues to grow at an increasing rate, it is becoming more difficult for a curator of a variant knowledge base to keep up with and curate all the published papers. Here, we suggest a deep learning-based literature triage method for genomic variation resources. Our method achieves state-of-the-art performance on the triage task. Moreover, our model does not require any laborious preprocessing or feature engineering steps, which are required for traditional machine learning triage methods. We applied our method to the literature triage process of UniProtKB/Swiss-Prot and the NHGRI-EBI GWAS Catalog for genomic variation by collaborating with the database curators. Both the manual curation teams confirmed that our method achieved higher precision than their previous query-based triage methods without compromising recall. Both results show that our method is more efficient and can replace the traditional query-based triage methods of manually curated databases. Our method can give human curators more time to focus on more challenging tasks such as actual curation as well as the discovery of novel papers/experimental techniques to consider for inclusion.
- Subjects :
- 0301 basic medicine
Computer science
Knowledge Bases
Information Storage and Retrieval
Convolutional neural network
Computer Applications
Machine Learning
Database and Informatics Methods
Databases, Genetic
Biology (General)
Databases, Protein
Data Curation
Ecology
Artificial neural network
Applied Mathematics
Simulation and Modeling
Data Curation/methods
Data Curation/statistics & numerical data
Deep Learning
Genomics
Information Storage and Retrieval/methods
Publications
Genomic Databases
Computational Theory and Mathematics
Modeling and Simulation
Physical Sciences
Algorithms
Research Article
Computer and Information Sciences
Neural Networks
Process (engineering)
QH301-705.5
Research and Analysis Methods
03 medical and health sciences
Cellular and Molecular Neuroscience
Machine Learning Algorithms
Artificial Intelligence
Support Vector Machines
Genome-Wide Association Studies
Genetics
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Information retrieval
Data curation
business.industry
Deep learning
Biology and Life Sciences
Computational Biology
Human Genetics
Genome Analysis
Triage
Support vector machine
030104 developmental biology
Biological Databases
Artificial intelligence
Catalogs
business
Precision and recall
Mathematics
Neuroscience
Subjects
Details
- Language :
- English
- ISSN :
- 15537358
- Volume :
- 14
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....08e0447a8884034554c00e1dd5d932ca