title: tmVar: a text mining approach for extracting sequence variants in biomedical literature.
abstract: BACKGROUND
  Text-mining mutation information from the literature becomes a critical part of the bioinformatics approach for the analysis and interpretation of sequence variations in complex diseases in the post-genomic era. It has also been used for assisting the creation of disease-related mutation databases. Most of existing approaches are rule-based and focus on limited types of sequence variations, such as protein point mutations. Thus, extending their extraction scope requires significant manual efforts in examining new instances and developing corresponding rules. As such, new automatic approaches are greatly needed for extracting different kinds of mutations with high accuracy.
  
  
  RESULTS
  Here, we report tmVar, a text-mining approach based on conditional random field (CRF) for extracting a wide range of sequence variants described at protein, DNA and RNA levels according to a standard nomenclature developed by the Human Genome Variation Society. By doing so, we cover several important types of mutations that were not considered in past studies. Using a novel CRF label model and feature set, our method achieves higher performance than a state-of-the-art method on both our corpus (91.4 versus 78.1% in F-measure) and their own gold standard (93.9 versus 89.4% in F-measure). These results suggest that tmVar is a high-performance method for mutation extraction from biomedical literature.
  
  
  BACKGROUND
  tmVar software and its corpus of 500 manually curated abstracts are available for download at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/pub/tmVar
authors.affiliation: National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), 8600 Rockville Pike, Bethesda, MD 20894, USA.
authors.forename: Chih-Hsuan
authors.initials: CH
authors.lastname: Wei
authors.forename: Bethany R
authors.initials: BR
authors.lastname: Harris
authors.forename: Hung-Yu
authors.initials: HY
authors.lastname: Kao
authors.forename: Zhiyong
authors.initials: Z
authors.lastname: Lu
mesh_terms: D001185:Artificial Intelligence; D004252:DNA Mutational Analysis / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D006801:Humans; D009154:Mutation; D012984:Software
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