ArticleBioinformatics (Oxford, England)2017
nala: text mining natural language mutation mentions.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- ViMRT: a text-mining tool and search engine for automated virus mutation recognition.Bioinformatics (Oxford, England) · 2023Article
- tmVar 3.0: an improved variant concept recognition and normalization tool.Bioinformatics (Oxford, England) · 2022Article
- Understanding the genetics of viral drug resistance by integrating clinical data and mining of the scientific literature.Scientific reports · 2022Article
- Accelerated variant curation from scientific literature using biomedical text mining.microPublication biology · 2022Article
- Automatic annotation of protein residues in published papers.Acta crystallographica. Section F, Structural biology communications · 2019Article
- Overview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine.Database : the journal of biological databases and curation · 2019Article
- Exploiting and assessing multi-source data for supervised biomedical named entity recognition.Bioinformatics (Oxford, England) · 2018Article
- LitVar: a semantic search engine for linking genomic variant data in PubMed and PMC.Nucleic acids research · 2018Article
- LocText: relation extraction of protein localizations to assist database curation.BMC bioinformatics · 2018Article
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10 authors.
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Abstract
motivationThe extraction of sequence variants from the literature remains an important task. Existing methods primarily target standard (ST) mutation mentions (e.g. 'E6V'), leaving relevant mentions natural language (NL) largely untapped (e.g. 'glutamic acid was substituted by valine at residue 6').
resultsWe introduced three new corpora suggesting named-entity recognition (NER) to be more challenging than anticipated: 28-77% of all articles contained mentions only available in NL. Our new method nala captured NL and ST by combining conditional random fields with word embedding features learned unsupervised from the entire PubMed. In our hands, nala substantially outperformed the state-of-the-art. For instance, we compared all unique mentions in new discoveries correctly detected by any of three methods (SETH, tmVar, or nala ). Neither SETH nor tmVar discovered anything missed by nala , while nala uniquely tagged 33% mentions. For NL mentions the corresponding value shot up to 100% nala -only. AVAILABILITY AND IMPLEMENTATION: Source code, API and corpora freely available at: http://tagtog.net/-corpora/IDP4+ . CONTACT: nala@rostlab.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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