Format

Send to

Choose Destination
Bioinformatics. 2016 Sep 15;32(18):2883-5. doi: 10.1093/bioinformatics/btw234. Epub 2016 Jun 2.

SETH detects and normalizes genetic variants in text.

Author information

1
Language Technology Lab, DFKI Berlin, Germany Knowledge Management in Bioinformatics, Institute for Computer Science, Humboldt-Universität Zu Berlin, Unter Den Linden 6, Berlin 10099, Germany.
2
University College London, Gower Street, LondonWC1E 6BT, UK.
3
Illumina, Inc, 451 El Camino Real, Santa Clara, CA 95050, USA.
4
Knowledge Management in Bioinformatics, Institute for Computer Science, Humboldt-Universität Zu Berlin, Unter Den Linden 6, Berlin 10099, Germany.

Abstract

: Descriptions of genetic variations and their effect are widely spread across the biomedical literature. However, finding all mentions of a specific variation, or all mentions of variations in a specific gene, is difficult to achieve due to the many ways such variations are described. Here, we describe SETH, a tool for the recognition of variations from text and their subsequent normalization to dbSNP or UniProt. SETH achieves high precision and recall on several evaluation corpora of PubMed abstracts. It is freely available and encompasses stand-alone scripts for isolated application and evaluation as well as a thorough documentation for integration into other applications.

AVAILABILITY AND IMPLEMENTATION:

SETH is released under the Apache 2.0 license and can be downloaded from http://rockt.github.io/SETH/ CONTACT: thomas@informatik.hu-berlin.de or leser@informatik.hu-berlin.de.

PMID:
27256315
DOI:
10.1093/bioinformatics/btw234
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Silverchair Information Systems
Loading ...
Support Center