SETH detects and normalizes genetic variants in text

Philippe Thomas, Tim Rocktäschel, Jörg Hakenberg, Yvonne Lichtblau, Ulf Leser

In: Bioinformatics 32 18 Seiten 2883-2885 Oxford University Press 2016.


Summary: 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 or

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Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence