Mining clinical attributes of genomic variants through assisted literature curation in Egas

Matos, Sérgio, Campos, David, Pinho, Renato, Silva, Raquel M., Mort, Matthew, Cooper, David Neil ORCID: https://orcid.org/0000-0002-8943-8484 and Oliveira, José Luís 2016. Mining clinical attributes of genomic variants through assisted literature curation in Egas. Database 2016 , baw096. 10.1093/database/baw096

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Abstract

The veritable deluge of biological data over recent years has led to the establishment of a considerable number of knowledge resources that compile curated information extracted from the literature and store it in structured form, facilitating its use and exploitation. In this article, we focus on the curation of inherited genetic variants and associated clinical attributes, such as zygosity, penetrance or inheritance mode, and describe the use of Egas for this task. Egas is a web-based platform for text-mining assisted literature curation that focuses on usability through modern design solutions and simple user interactions. Egas offers a flexible and customizable tool that allows defining the concept types and relations of interest for a given annotation task, as well as the ontologies used for normalizing each concept type. Further, annotations may be performed on raw documents or on the results of automated concept identification and relation extraction tools. Users can inspect, correct or remove automatic text-mining results, manually add new annotations, and export the results to standard formats. Egas is compatible with the most recent versions of Google Chrome, Mozilla Firefox, Internet Explorer and Safari and is available for use at https://demo.bmd-software.com/egas/.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Medicine
Subjects:	Q Science > QH Natural history > QH426 Genetics
Publisher:	Oxford University Press
ISSN:	1758-0463
Related URLs:	https://demo.bmd-software.com/egas/
Date of First Compliant Deposit:	9 June 2016
Date of Acceptance:	15 May 2016
Last Modified:	05 May 2023 07:50
URI:	https://orca.cardiff.ac.uk/id/eprint/91672

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