ArticlePLoS computational biology2018
Scaling up data curation using deep learning: An application to literature triage in genomic variation resources.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed.
- Artificial Intelligence Transforming Post-Translational Modification Research.Bioengineering (Basel, Switzerland) · 2024Review
- Unsupervised literature mining approaches for extracting relationships pertaining to habitats and reproductive conditions of plant species.Frontiers in artificial intelligence · 2024Article
- Article
- Automatic identification of scientific publications describing digital reconstructions of neural morphology.Brain informatics · 2023Article
- From function to translation: Decoding genetic susceptibility to human diseases via artificial intelligence.Cell genomics · 2023Review
- Ensemble of deep learning language models to support the creation of living systematic reviews for the COVID-19 literature.Systematic reviews · 2023Article
- Automatic identification of scientific publications describing digital reconstructions of neural morphology.bioRxiv : the preprint server for biology · 2023Article
- Deep learning in bioinformatics.Turkish journal of biology = Turk biyoloji dergisi · 2023Article
- Assigning species information to corresponding genes by a sequence labeling framework.Database : the journal of biological databases and curation · 2022Article
- A roadmap for the functional annotation of protein families: a community perspective.Database : the journal of biological databases and curation · 2022Article
- Performance of Multiple Pretrained BERT Models to Automate and Accelerate Data Annotation for Large Datasets.Radiology. Artificial intelligence · 2022Article
- Managing the life cycle of a portfolio of open data resources at the SIB Swiss Institute of Bioinformatics.Briefings in bioinformatics · 2022Review
- Sequencing-based genome-wide association studies reporting standards.Cell genomics · 2021Article
- LitSuggest: a web-based system for literature recommendation and curation using machine learning.Nucleic acids research · 2021Article
- Recent advances of automated methods for searching and extracting genomic variant information from biomedical literature.Briefings in bioinformatics · 2021Review
- LitCovid: an open database of COVID-19 literature.Nucleic acids research · 2021Article
- Gynura divaricata exerts hypoglycemic effects by regulating the PI3K/AKT signaling pathway and fatty acid metabolism signaling pathway.Nutrition & diabetes · 2020Article
- SIB Literature Services: RESTful customizable search engines in biomedical literature, enriched with automatically mapped biomedical concepts.Nucleic acids research · 2020Article
- BioConceptVec: Creating and evaluating literature-based biomedical concept embeddings on a large scale.PLoS computational biology · 2020Article
- Integrating image caption information into biomedical document classification in support of biocuration.Database : the journal of biological databases and curation · 2020Article
Corrections and comments
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Authors and funding
11 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.