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A machine learning framework for discovery and enrichment of metagenomics metadata from open access publications
Open accessPreprint — published as 10.1093/gigascience/giac077 · Paper page
Abstract
Metagenomics is a culture-independent method for studying the microbes inhabiting a particular environment. Comparing the composition of samples (functionally/taxonomically), either from a longitudinal study or cross-sectional studies can provide clues into how the microbiota has adapted to the environment. However, a recurring challenge, especially when comparing results between independent studies, is that key metadata about the sample and molecular methods used to extract and sequence the genetic material are often missing from sequence records, making it difficult to account for confounding factors. Nevertheless, this missing metadata may be found in the narrative of publications describing the research. Here, we describe a machine learning framework that automatically extracts essential metadata for a wide range of metagenomics studies from the literature contained in Europe PMC. This framework has enabled the extraction of metadata from 114,099 publications in Europe PMC, including 19,900 publications describing metagenomics studies in ENA and MGnify.
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
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Metadata
- Authors
- Nassar, M., Rogers, A. B., Talo', F., Sanchez, S., Shafique, Z., Finn, R. D., & McEntyre, J.
- Affiliations
- European Bioinformatics Institute (GB)
- EMBL's European Bioinformatics Institute (EMBL-EBI)
- Corresponding author
- McEntyre, Johanna
- ORCID
- Venue
- GigaScience
- Published
- 2022-02-25
- Volume
- 11
- ISSN
- 2047-217X (ISSN-L)
- PMC
- PMC9366992
- OpenAlex
- W4291017647
- Cited by
- 14
- References
- 1
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 2 · 2023: 4 · 2022: 2
- FWCI
- 1.06
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- GigaScience (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust (221523)
- European Bioinformatics Institute (221523)
- Directorate for Biological Sciences
- Biotechnology and Biological Sciences Research Council (BB/S009043/1)
- Topics
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Keywords
- machine learning, open access publications, Europe PMC, MGnify, European Nucleotide Archive, metadata enrichment, confounding factors
- MeSH terms
- Machine Learning, Metadata, Cross-Sectional Studies, Longitudinal Studies, Access to Information, Metagenomics
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