Literature DB >> 27256315

SETH detects and normalizes genetic variants in text.

Philippe Thomas1, Tim Rocktäschel2, Jörg Hakenberg3, Yvonne Lichtblau4, Ulf Leser4.   

Abstract

UNLABELLED: : 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.
© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Mesh:

Year:  2016        PMID: 27256315     DOI: 10.1093/bioinformatics/btw234

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  16 in total

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Journal:  Sci Rep       Date:  2018-09-06       Impact factor: 4.379

3.  ResidueFinder: extracting individual residue mentions from protein literature.

Authors:  Ton E Becker; Eric Jakobsson
Journal:  J Biomed Semantics       Date:  2021-07-21

4.  tmVar 2.0: integrating genomic variant information from literature with dbSNP and ClinVar for precision medicine.

Authors:  Chih-Hsuan Wei; Lon Phan; Juliana Feltz; Rama Maiti; Tim Hefferon; Zhiyong Lu
Journal:  Bioinformatics       Date:  2018-01-01       Impact factor: 6.937

5.  RegEl corpus: identifying DNA regulatory elements in the scientific literature.

Authors:  Samuele Garda; Freyda Lenihan-Geels; Sebastian Proft; Stefanie Hochmuth; Markus Schülke; Dominik Seelow; Ulf Leser
Journal:  Database (Oxford)       Date:  2022-06-27       Impact factor: 4.462

6.  nala: text mining natural language mutation mentions.

Authors:  Juan Miguel Cejuela; Aleksandar Bojchevski; Carsten Uhlig; Rustem Bekmukhametov; Sanjeev Kumar Karn; Shpend Mahmuti; Ashish Baghudana; Ankit Dubey; Venkata P Satagopam; Burkhard Rost
Journal:  Bioinformatics       Date:  2017-06-15       Impact factor: 6.937

7.  SNPPhenA: a corpus for extracting ranked associations of single-nucleotide polymorphisms and phenotypes from literature.

Authors:  Behrouz Bokharaeian; Alberto Diaz; Nasrin Taghizadeh; Hamidreza Chitsaz; Ramyar Chavoshinejad
Journal:  J Biomed Semantics       Date:  2017-04-07

8.  The SNPcurator: literature mining of enriched SNP-disease associations.

Authors:  Noha S Tawfik; Marco R Spruit
Journal:  Database (Oxford)       Date:  2018-01-01       Impact factor: 3.451

9.  Unique insights from ClinicalTrials.gov by mining protein mutations and RSids in addition to applying the Human Phenotype Ontology.

Authors:  Shray Alag
Journal:  PLoS One       Date:  2020-05-27       Impact factor: 3.240

10.  LitVar: a semantic search engine for linking genomic variant data in PubMed and PMC.

Authors:  Alexis Allot; Yifan Peng; Chih-Hsuan Wei; Kyubum Lee; Lon Phan; Zhiyong Lu
Journal:  Nucleic Acids Res       Date:  2018-07-02       Impact factor: 16.971

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