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PheneBank: a literature-based database of phenotypes.
Pilehvar, Mohammad Taher; Bernard, Adam; Smedley, Damian; Collier, Nigel.
Afiliación
  • Pilehvar MT; Language Technology Lab, Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge, UK.
  • Bernard A; The William Harvey Research Institute, Queen Mary University of London, London, UK.
  • Smedley D; The William Harvey Research Institute, Queen Mary University of London, London, UK.
  • Collier N; Language Technology Lab, Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge, UK.
Bioinformatics ; 38(4): 1179-1180, 2022 01 27.
Article en En | MEDLINE | ID: mdl-34788791
ABSTRACT
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.
Asunto(s)

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Programas Informáticos / Enfermedades Raras Límite: Humans Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2022 Tipo del documento: Article País de afiliación: Reino Unido

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Programas Informáticos / Enfermedades Raras Límite: Humans Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2022 Tipo del documento: Article País de afiliación: Reino Unido