Motivation: Significant effort has been spent by curators to create coding systems for phenotypes such as the Human Phenotype Ontology, as well as disease-phenotype annotations. We aim to support the discovery of literature-based phenotypes and integrate them into the knowledge discovery process.
Results: PheneBank is a Web-portal for retrieving human phenotype-disease associations that have been text-mined from the whole of Medline. Our approach exploits state-of-the-art machine learning for concept identification by utilizing an expert annotated rare disease corpus from the PMC Text Mining subset. Evaluation of the system for entities is conducted on a gold-standard corpus of rare disease sentences and for associations against the Monarch initiative data.
Availability And Implementation: The PheneBank Web-portal freely available at http://www.phenebank.org. Annotated Medline data is available from Zenodo at DOI: 10.5281/zenodo.1408800. Semantic annotation software is freely available for non-commercial use at GitHub: https://github.com/pilehvar/phenebank.
Supplementary Information: Supplementary data are available at Bioinformatics online.
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Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8796364 | PMC |
http://dx.doi.org/10.1093/bioinformatics/btab740 | DOI Listing |
Bioinformatics
January 2022
Language Technology Lab, Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge, UK.
Motivation: Significant effort has been spent by curators to create coding systems for phenotypes such as the Human Phenotype Ontology, as well as disease-phenotype annotations. We aim to support the discovery of literature-based phenotypes and integrate them into the knowledge discovery process.
Results: PheneBank is a Web-portal for retrieving human phenotype-disease associations that have been text-mined from the whole of Medline.
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