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PheneBank: a literature-based database of phenotypes.

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Pilehvar, Mohammad Taher 
Bernard, Adam 
Smedley, Damian 

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.

Description

Keywords

Humans, Rare Diseases, Software, Algorithms, Data Mining, Phenotype

Journal Title

Bioinformatics

Conference Name

Journal ISSN

1367-4803
1367-4811

Volume Title

Publisher

Oxford University Press (OUP)
Sponsorship
Engineering and Physical Sciences Research Council (EP/M005089/1)
Medical Research Council (MR/M025160/1)
Medical Research Council (grant MR/M025160/1).